GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES

Size: px
Start display at page:

Download "GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES"

Transcription

1 7 AIROnews VII, n.4 - Winter 2002 GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES by Paola Festa Abstract. GRASP is an iterative multi-start metaheuristic for solving difficult combinatorial problems. Each GRASP iteration consists of two phases: a greedy adaptive randomized construction phase and a local search phase. Starting from the feasible solution built during the greedy adaptive randomized construction phase, the local search explores its neighborhood until a local optimum is found. The best solution found overall the different iterations is kept as the result. In this paper, the basic components of GRASP are described in detail. Successful implementations are discussed together with improved and alternative solution construction mechanisms as well as techniques proposed in literature for speeding up the search, hybridizations with other metaheuristics, and intensification and post-optimization strategies using pathrelinking. 1. Introduction and description of a generic GRASP Many combinatorial problems are intrinsically difficult in the sense that to be solved they require a number of elementary steps which is exponential in the size of the their input. Given their intrinsic hardness, usually such problems are approached heuristically, i.e. they are solved by applying either a heuristic algorithm or a metaheuristic schema, that find good suboptimal solutions in reasonable computational time. The most promising of such techniques include among others Simulated Annealing [Kirkpatrick, 1994], Tabù Search [Glover, 1989; Glover, 1990; Glover & Laguna, 1997], Genetic Algorithms [Goldberg, 1989], Variable Neighborhood Search [Hansen & Mladenovìc, 1998], and GRASP (Greedy Randomized Adaptive Search Procedures) [Feo & Resende, 1995]. GRASP is an iterative multi-start algorithm, each iteration consisting of two phases: a greedy adaptive randomized construction phase and a local search phase. Starting from the feasible solution built during the greedy adaptive randomized construction phase, the local search explores its neighborhood until a local optimum is found. The best solution found overall the different iterations is kept as the result. An extensive survey of GRASP and its applications can be found in [Festa & Resende, 2002]. Paola Festa In the following, we shall refer to a generic optimization problem, defined by a finite ground set E={1,2,,n}, a set of feasible solutions F 2 Ε, and an objective function f:2 Ε R to be minimized. The target is to find an optimal solution Ŝ in F, such that f(ŝ) f (S), for each S in F. In Figure 1 the pseudocode of a generic GRASP for a minimization problem is given. The parameters MAX_ITERATIONS and SEED are the maximum number of performed iterations and the initial seed used by the pseudorandom number generator, respectively. Starting from an empty solution, in the construction phase a feasible solution is iteratively constructed, one element at a time. The basic GRASP construction phase is similar to the semi-greedy heuristic proposed independently by [Hart & Shogan, 1987]. At each construction iteration, the choice of the next element to be added is determined by ordering all candidate elements (i.e. those that can be added to the solution without violating feasibility) in a candidate list C with respect to a greedy function g:c R, that measures the (myopic) benefit of selecting each element. Usually, the greedy function takes into account the incremental cost in term of objective function value associated with the

2 8 AIROnews VII, n.4 - Winter 2002 Procedure GRASP (MAX_ITERATIONS, SEED) Best_Solution = 0; Read_Input ( ); for k = 1,2,, MAX_ITERATIONS do Solution = GreedyRandomizedConstruction (SEED); Solution = LocalSearch (Solution); if (Solution is better than Best_Solution) then UpdateSolution (Solution, Best_Solution); endif endfor return (Best_Solution); end GRASP Figure 1. Pseudocode of a generic GRASP incorporation of a candidate into the solution under construction. The heuristic is adaptive because the benefits associated with every element are updated at each iteration of the construction phase to reflect the changes brought on by the selection of the previous element. The probabilistic component of a GRASP is characterized by randomly choosing one of the best candidates in the list, but not necessarily the top candidate. The list of best candidates is called the restricted candidate list (RCL). This choice technique allows Procedure GreedyRandomizedConstruction (SEED) Solution = 0; Sort the candidate elements according to their incremantal costs; while Solution is not complete do Build the Restricted Candidate List (RCL); Select from RCL an element v at random; Solution = Solution {v}; Resort the candidate elements according to their incremental costs; endwhile return (Solution); end GreedyRandomizedConstruction Figure 2. Pseudocode of a generic GRASP construction phase for different solutions to be obtained at each GRASP iteration, but does not necessarily compromise the power of the adaptive greedy component of the method. Figure 2 describes the pseudocode of a generic construction phase. As is the case for many deterministic methods, the solutions generated by a GRASP construction are not guaranteed to be locally optimal with respect to simple neighborhood definitions. Hence, it is almost always beneficial to apply a local search to attempt to improve each constructed solution. A local search algorithm works in an iterative fashion by successively replacing the current solution by a better solution in the neighborhood of the current solution. It terminates when no better solution is found in the neighborhood. The neighborhood structure N for a problem Procedure LocalSearch (Solution) while Solution is not locally optimal do Find s N such that f(s ) f(solution ); Solution = s ; endwhile return (Solution); end LocalSearch Figure 3. Pseudocode of a generic local search phase P relates a solution s of the problem to a subset of solutions N(s). A solution is said to be locally optimal if there is no better solution in N(s). The key to success for a local search algorithm consists of the suitable choice of a neighborhood structure, efficient neighborhood search techniques, the fast evaluation of the cost function, and the quality of the starting solution itself, which depends on the construction phase. Figure 3 describes the pseudocode of a generic local search phase. The local search may be implemented using either a best improving or a first improving strategy. The first improving strategy stops as soon as an improving neighbor is found, while in the best improving strategy all neighbors are evaluated and the best among them is kept as new current solution. Generally, such local optimization procedures can require exponential time [Johnson et al., 1988] from an arbitrary starting point, empirically their

3 9 AIROnews VII, n.4 - Winter 2002 efficiency significantly improves as the initial solution improves. The result is that often many GRASP solutions are generated in the same amount of time required for the local optimization procedure to converge from a single random start. Furthermore, the best of these GRASP solutions is generally significantly better than the single solution obtained from a random starting point. 2. Restricted Candidate List (RCL) construction mechanisms Let c(x) be the incremental cost of element x, that is candidate to be inserted in the solution under construction. At each GRASP iteration, let c min and c max be the smallest and the largest incremental costs, respectively. Then, in the GRASP construction framework, the RCL is made up of all candidates with the best incremental costs and it can be limited either by the number of elements (cardinality-based criterion) or by their quality (value-based criterion). If the cardinalitybased criterion is chosen, then the cardinality of RCL is a priori fixed to some p and the RCL is made up of those elements having the p best costs, while in the valuebased case, the cardinality of RCL depends on a threshold parameter α [0,1]. In more detail, RCL is formed by all possibile candidates x whose incremental cost c(x) [c min,c min +α(c max -c min )]. Note that, the case α=0 corresponds to a pure greedy strategy, while the case α=1 is equivalent to a completely random strategy. One possibile shortcoming of the basic GRASP construction framework is the lack of memory of the history of solutions found in previous iterations. A first possibile strategy to introduce in GRASP a learning mechanism has been proposed by Prais and Ribeiro [Prais & Ribeiro, 2000]. They proposed an extension of the basic GRASP schema, called Reactive GRASP in which the parameter α is self-tuned and its value is periodically modified according to the quality of the solutions computed in the recent iterations. The authors have shown that their reactive approach leads to improvements over the pure GRASP thanks to the greater diversification and less reliance on α parameter tuning. Reactive GRASP have been successfully applied in traffic assignment problems [Prais & Ribeiro, 2000], power system transmission network planning [Binato & Oliveira, 2002], and in a capacitated location problem [Delmaire et al., 1999]. Another step toward an improved and alternative solution construction mechanism is to allow cost perturbations. The idea is to introduce some noise in the original costs in a fashion that resembles the noising method of Charon and Hudry [Charon & Hudry, 1993, 2002]. Cost perturbations are effective in all cases when for the problem to be solved there is available no greedy algorithm for randomization or the construction algorithm is not very sensitive to randomization. This type of approach has been proposed in literature for several problems, including Steiner tree problems [Canuto et al., 2001], [Ribeiro et al., 2002]. Another construction mechanism has been proposed by Bresina [Bresina, 1996]. Once built the RCL, instead of choosing with equal probability one candidate among the RCL elements, Bresina introduced a family of probability distributions to bias the selection toward some particular candidates. Several different bias functions have been introduced. They are based on the rank r(x) assigned to each candidate x according to its greedy function value and are evaluated only for the elements in RCL. A successful application of Bresina s bias function can be found in [Binato et al., 2002]. In [Fleurent & Glover, 1999], the authors observed that imperfections introduced during basic GRASP construction iterations can be corrected by performing local search during the construction itself and not only starting from an already built feasible solution. This mechanism is known as Proximate Optimality Principle. Because of efficiency requirements, it can not be applied at each iteration. In [Binato et al., 2002] local search has been performed after 40% and 80% of the construction moves have been taken, as well as at the end of the construction phase. 3. Hybridizations with other metaheuristics As enhancements to its basic pure framework, hybridizations of GRASP with several other metaheuristics have been studied. In this section, some of them are surveyed and very briefly described. Laguna and Gonzalez-Velarde have first studied hybridization of GRASP with Tabù Search [Laguna & Gonzalez-Velarde, 1991], while in [Delmaire et al., 1999] the authors proposed a Reactive GRASP whose local search have been strengthened by Tabù Search.

4 10 AIROnews VII, n.4 - Winter 2002 GRASP has been used also in conjunction with Genetic Algorithms. Basically, the feasible solution found by using a GRASP construction phase has been used as initial population by a Genetic Algorithm, as for example in [Armony et al., 2000] and in [Ahuja et al., 2000], where a greedy genetic algorithm is proposed for the quadratic assignment problem. Another interesting hybridization of GRASP involves VNS (Variable Neighborhood Search) and Variable Neighborhood Descent (VND) [Hansen & Mladenovic, 1998]. Almost all randomization effort in GRASP is in the construction phase, while in VNS and VND to escape from local minima all randomization effort involves the local search. Therefore, GRASP and VNS- VND can be viewed as complementary approaches, whose combination can potentially lead to effective methods. A first attempt in this direction has been done by Martins et al. in [Martins et al., 2000] for the Steiner tree problem in graphs. Recently, Festa et al. in [Festa et al., 2003] studied and tested several different variants and combinations of GRASP and VNS for the Max-Cut problem, finding and improving the best known solutions for some open instances from the literature. They also studied the combination of GRASP with Pathrelinking, another enhancement to the pure GRASP schema that leads to improvements in both robustness and quality of the solutions. Path-relinking has been proposed by Glover in [Glover, 1996] as an intensification strategy exploring trajectories connecting elite solutions obtained by tabu search or scatter search. Basically, starting from one or more elite solutions, Pathrelinking generates paths in the solution space leading towards other guiding elite solutions and explores them in the search for better solutions. This is simply accomplished by selecting moves that introduce attributes contained in the guiding solutions. Pathrelinking within a GRASP algorithm can be used either as intensification or as post-optimization strategy. If used as intensification strategy, it is applied at each GRASP iteration after the local search phase, while as postoptimization, it is applied only on the GRASP output solution. Successful applications of Path-relinking combined with GRASP are described in [Aiex et al., 2000], [Aiex et al., 2003], [Canuto et al., 2001], [Laguna & Martì, 1999], [Resende & Ribeiro, 2001], [Ribeiro et al., 2002], while different implementation strategies are described and investigated in detail by Resende and Ribeiro in [Resende & Ribeiro, 2001]. 4. Concluding remarks In this paper, the basic components of GRASP methodology have been described. Improved and alternative solution construction mechanisms have been discussed as well as techniques proposed in literature for speeding up the search, hybridizations with other metaheuristics, and intensification and post-optimization strategies using path-relinking. It is difficult to formally analyze the quality of solution values found by using GRASP. However, there is an intuitive justification that views GRASP as a repetitive sampling technique, as explained in the following. Each GRASP iteration produces a sample solution from an unknown distribution of all obtainable results. The mean and variance of the distribution are functions of the restrictive nature of the candidate list. For example, if the RCL contains only one element, then only one solution will be produced and the variance of the distribution will be zero. Given an effective greedy function, the mean solution value in this case should be good, but probably suboptimal. If a less restrictive cardinality limit is imposed, many different solutions will be produced implying a larger variance. Since the greedy function is more compromised in this case, the mean solution value should degrade. Intuitively, however, by order statistics and the fact that the samples are randomly produced, the best value found should outperform the mean value. Indeed, often the best solutions sampled are optimal. GRASP methodology has three further attractive characteristics: 1) it can be easily implemented; 2) contrary to other metaheuristics, in its basic version it needs the adjustment of the only parameter α; 3) it can be trivially implemented in parallel. In the simplest parallel implementation of GRASP, each processor is initialized with its own copy of the procedure, the instance data, and an independent random number sequence. The GRASP iterations are then performed in parallel with only a single global variable required to store the best solution found over all processors. GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing and turbine balancing.

5 11 AIROnews VII, n.4 - Winter 2002 The reader can refer to [Festa & Resende, 2002], which contains an annotated bibliography of the GRASP literature from 1989 to Paola Festa Dipartimento di Matematica e Applicazioni Università degli Studi di Napoli FEDERICO II paola.festa@unina.it References - [Aiex et al., 2000] Aiex R.M., M.G.C. Resende, P.M. Pardalos, and G. Toraldo, GRASP with Path-relinking for the three-index assignment problem, TR - AT&T Labs- Research, Florham Park, NJ USA, [Aiex et al., 2003] Aiex R.M., S. Binato, and M.G.C. Resende, Parallel GRASP with Path-relinking for job shop scheduling, to appear on Parallel Computing, [Ahuja et al., 2000] Ahuja R.K., J.B. Orlin, and A. Tiwari, A greedy genetic algorithm for the quadratic assignment problem, Computers and Operations Research, 27: , [Armony et al., 2000] Armony M. J.C. Klincewicz, H. Luss, and M.B. Rosenwein, Design of stacked self-healing rings using a genetic algorithm, J. of Heuristics, 6:85-105, [Binato & Oliveira, 2002] Binato S. and G.C. Oliveira, A Reactive GRASP for transmission network expansion planning, in C.C. Ribeiro and P. Hansen, editors, Essay and Surveys in Metaheuristics, , Kluwer Academic Publishers, [Binato et al., 2002] Binato S., W.J. Hery, D. Loewenstern, and M.G.C. Resende, A GRASP for job shop scheduling, in C.C. Ribeiro and P. Hansen, editors, Essay and Surveys in Metaheuristics, , Kluwer Academic Publishers, [Bresina, 1996] Bresina J.L., Heuristic-biased stochastic sampling, in Proceedings in the 13 th National Conference on Artificial Intelligence, , Portland, [Canuto et al., 2001] Canuto S.A., M.G.C. Resende, and C.C. Ribeiro, Local search with perturbations for the prizecollecting Steiner tree problem in graphs, Networks, 38:50-58, [Charon & Hudry, 1993] Charon I. and O. Hudry, The noising method: a new method for combinatorial optimization, Operations Research Letters, 14: , [Charon & Hudry, 2002] Charon I. and O. Hudry, The noising method: a survey, in C.C. Ribeiro and P. Hansen, editors, Essay and Surveys in Metaheuristics, , Kluwer Academic Publishers, [Delmaire et al., 1999] Delmaire H., J.A. Dìaz, E. Fernandez and M. Ortega, Reactive GRASP and tabù search based heuristics for the single source capacitated plant location problem, INFOR, 37: , [Feo & Resende, 1995] Feo T.A. and M.G.C. Resende. Greedy Randomized Adaptive Search Procedures, J. of Global Optimization, 6: , [Festa & Resende, 2002] Festa P. and M.G.C. Resende. GRASP: An annotated bibliography, in C.C. Ribeiro and P. Hansen, editors, Essay and Surveys in Metaheuristics, , Kluwer Academic Publishers, [Festa et al., 2003] Festa P., P.M. Pardalos, M.G.C. Resende, and C.C. Ribeiro, Randomized heuristics for the max-cut problem, to appear on Optimization Methods and Software, [Fleurent & Glover, 1999] Fleurent C. and F. Glover, Improved constructive multistart strategies for the quadratic assignment problem using adaptive memory, INFORMS J. on Computing, 11: , [Kirkpatrick, 1994] Kirkpatrack S. Optimization by Simulated Annealing: Quantitative Studies, J. of Statistical Physics, 34: , [Glover, 1989] Glover F. Tabù search Part I, ORSA J. on Computing, 1: , [Glover, 1990] Glover F. Tabù search Part II, ORSA J. on Computing, 2:4-32, [Glover, 1996] Glover F., Tabù search and adaptive memory programming - Advances, applications, and challenges, in R.S. Barr, R.V. Helgason, and J.L. Kennington, editors, Interfaces in Computer Science and Operations research, 1-75, Kluwer Academic Publishers, [Glover & Laguna, 1997] Glover F. and M. Laguna. Tabù search, Kluwer Academic Publishers, [Goldberg, 1989] Goldberg D.E. Genetic algorithms in search, optimization and machine learning", Addison- Wesley, [Hansen & Mladenovìc, 1998] Hansen P. and N. Mladenovìc, An introduction to variable neighborhood search, in S. Voss and S. Martello and I. H. Osman and C. Roucairol, editors, Meta-heuristics, Advances and trends in local search paradigms for optimization, Kluwer Academic Publishers, [Hart & Shogan, 1987] Hart J.P. and A.W. Shogan, Semigreedy heuristics: An empirical study, Operations Research Letters, 6: , [Johnson et al., 1988] Johnson D.S., C.H. Papadimitriou, and M. Yannakakis, How Easy is Local Search?, J. of Computer and System Sciences, 17:79-100, [Laguna & Gonzalez-Velarde, 1991] Laguna M. and J.L. Gonzalez-Velarde, A search heuristic for just-in-time scheduling in parallel machine, J. of Intelligent Manufacturing, 2: , [Laguna & Martì, 1999] M. Laguna and R. Martì, GRASP and Path relinking for 2-layer straight line crossing minimization, INFORMS Journal on Computing, 11:44-52, 1999.

ISFA2008U_120 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM

ISFA2008U_120 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM Proceedings of 28 ISFA 28 International Symposium on Flexible Automation Atlanta, GA, USA June 23-26, 28 ISFA28U_12 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM Amit Gil, Helman Stern, Yael Edan, and

More information

Reinforcement Learning by Comparing Immediate Reward

Reinforcement Learning by Comparing Immediate Reward Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate

More information

Research Article Hybrid Multistarting GA-Tabu Search Method for the Placement of BtB Converters for Korean Metropolitan Ring Grid

Research Article Hybrid Multistarting GA-Tabu Search Method for the Placement of BtB Converters for Korean Metropolitan Ring Grid Mathematical Problems in Engineering Volume 2016, Article ID 1546753, 9 pages http://dx.doi.org/10.1155/2016/1546753 Research Article Hybrid Multistarting GA-Tabu Search Method for the Placement of BtB

More information

Given a directed graph G =(N A), where N is a set of m nodes and A. destination node, implying a direction for ow to follow. Arcs have limitations

Given a directed graph G =(N A), where N is a set of m nodes and A. destination node, implying a direction for ow to follow. Arcs have limitations 4 Interior point algorithms for network ow problems Mauricio G.C. Resende AT&T Bell Laboratories, Murray Hill, NJ 07974-2070 USA Panos M. Pardalos The University of Florida, Gainesville, FL 32611-6595

More information

A Comparison of Annealing Techniques for Academic Course Scheduling

A Comparison of Annealing Techniques for Academic Course Scheduling A Comparison of Annealing Techniques for Academic Course Scheduling M. A. Saleh Elmohamed 1, Paul Coddington 2, and Geoffrey Fox 1 1 Northeast Parallel Architectures Center Syracuse University, Syracuse,

More information

Introduction to Simulation

Introduction to Simulation Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /

More information

Discriminative Learning of Beam-Search Heuristics for Planning

Discriminative Learning of Beam-Search Heuristics for Planning Discriminative Learning of Beam-Search Heuristics for Planning Yuehua Xu School of EECS Oregon State University Corvallis,OR 97331 xuyu@eecs.oregonstate.edu Alan Fern School of EECS Oregon State University

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive Neural Net Fuzzy Filtering: Basic Description Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:

More information

The Good Judgment Project: A large scale test of different methods of combining expert predictions

The Good Judgment Project: A large scale test of different methods of combining expert predictions The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Seminar - Organic Computing

Seminar - Organic Computing Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for

Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email Marilyn A. Walker Jeanne C. Fromer Shrikanth Narayanan walker@research.att.com jeannie@ai.mit.edu shri@research.att.com

More information

Cooperative evolutive concept learning: an empirical study

Cooperative evolutive concept learning: an empirical study Cooperative evolutive concept learning: an empirical study Filippo Neri University of Piemonte Orientale Dipartimento di Scienze e Tecnologie Avanzate Piazza Ambrosoli 5, 15100 Alessandria AL, Italy Abstract

More information

Softprop: Softmax Neural Network Backpropagation Learning

Softprop: Softmax Neural Network Backpropagation Learning Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science

More information

Investigating Ahuja-Orlin s Large Neighbourhood Search Approach for Examination Timetabling

Investigating Ahuja-Orlin s Large Neighbourhood Search Approach for Examination Timetabling Investigating Ahuja-Orlin s Large Neighbourhood Search Approach for Examination Timetabling SALWANI ABDULLAH 1, SAMAD AHMADI 2, EDMUND K. BURKE 1, MOSHE DROR 3 1 Automated Scheduling, Optimisation and

More information

A simulated annealing and hill-climbing algorithm for the traveling tournament problem

A simulated annealing and hill-climbing algorithm for the traveling tournament problem European Journal of Operational Research xxx (2005) xxx xxx Discrete Optimization A simulated annealing and hill-climbing algorithm for the traveling tournament problem A. Lim a, B. Rodrigues b, *, X.

More information

Team Formation for Generalized Tasks in Expertise Social Networks

Team Formation for Generalized Tasks in Expertise Social Networks IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust Team Formation for Generalized Tasks in Expertise Social Networks Cheng-Te Li Graduate

More information

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method Farhadi F, Sorkhi M, Hashemi S et al. An effective framework for fast expert mining in collaboration networks: A grouporiented and cost-based method. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(3): 577

More information

Axiom 2013 Team Description Paper

Axiom 2013 Team Description Paper Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association

More information

An OO Framework for building Intelligence and Learning properties in Software Agents

An OO Framework for building Intelligence and Learning properties in Software Agents An OO Framework for building Intelligence and Learning properties in Software Agents José A. R. P. Sardinha, Ruy L. Milidiú, Carlos J. P. Lucena, Patrick Paranhos Abstract Software agents are defined as

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and

More information

Measurability and Reproducibility in University Timetabling Research: Discussion and Proposals

Measurability and Reproducibility in University Timetabling Research: Discussion and Proposals Measurability and Reproducibility in University Timetabling Research: Discussion and Proposals Andrea Schaerf and Luca Di Gaspero Dipartimento di Ingegneria Elettrica, Gestionale e Meccanica Università

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate

More information

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,

More information

Rule Learning With Negation: Issues Regarding Effectiveness

Rule Learning With Negation: Issues Regarding Effectiveness Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United

More information

(Sub)Gradient Descent

(Sub)Gradient Descent (Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

More information

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

An Introduction to Simulation Optimization

An Introduction to Simulation Optimization An Introduction to Simulation Optimization Nanjing Jian Shane G. Henderson Introductory Tutorials Winter Simulation Conference December 7, 2015 Thanks: NSF CMMI1200315 1 Contents 1. Introduction 2. Common

More information

University of Groningen. Systemen, planning, netwerken Bosman, Aart

University of Groningen. Systemen, planning, netwerken Bosman, Aart University of Groningen Systemen, planning, netwerken Bosman, Aart IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document

More information

Exploration. CS : Deep Reinforcement Learning Sergey Levine

Exploration. CS : Deep Reinforcement Learning Sergey Levine Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,

More information

Regret-based Reward Elicitation for Markov Decision Processes

Regret-based Reward Elicitation for Markov Decision Processes 444 REGAN & BOUTILIER UAI 2009 Regret-based Reward Elicitation for Markov Decision Processes Kevin Regan Department of Computer Science University of Toronto Toronto, ON, CANADA kmregan@cs.toronto.edu

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

Transfer Learning Action Models by Measuring the Similarity of Different Domains

Transfer Learning Action Models by Measuring the Similarity of Different Domains Transfer Learning Action Models by Measuring the Similarity of Different Domains Hankui Zhuo 1, Qiang Yang 2, and Lei Li 1 1 Software Research Institute, Sun Yat-sen University, Guangzhou, China. zhuohank@gmail.com,lnslilei@mail.sysu.edu.cn

More information

Language properties and Grammar of Parallel and Series Parallel Languages

Language properties and Grammar of Parallel and Series Parallel Languages arxiv:1711.01799v1 [cs.fl] 6 Nov 2017 Language properties and Grammar of Parallel and Series Parallel Languages Mohana.N 1, Kalyani Desikan 2 and V.Rajkumar Dare 3 1 Division of Mathematics, School of

More information

Active Learning. Yingyu Liang Computer Sciences 760 Fall

Active Learning. Yingyu Liang Computer Sciences 760 Fall Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,

More information

Learning From the Past with Experiment Databases

Learning From the Past with Experiment Databases Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University

More information

Rule Learning with Negation: Issues Regarding Effectiveness

Rule Learning with Negation: Issues Regarding Effectiveness Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX

More information

On-Line Data Analytics

On-Line Data Analytics International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] On-Line Data Analytics Yugandhar Vemulapalli #, Devarapalli Raghu *, Raja Jacob

More information

GACE Computer Science Assessment Test at a Glance

GACE Computer Science Assessment Test at a Glance GACE Computer Science Assessment Test at a Glance Updated May 2017 See the GACE Computer Science Assessment Study Companion for practice questions and preparation resources. Assessment Name Computer Science

More information

Investigating Ahuja-Orlin's Large Neighbourhood Search for Examination Timetabling

Investigating Ahuja-Orlin's Large Neighbourhood Search for Examination Timetabling School of Computer Science and Information Technology University of Nottingham Jubilee Campus NOTTINGHAM NG8 1BB, UK Computer Science Technical Report No. NOTTCS-TR-2004-8 Investigating Ahuja-Orlin's Large

More information

CSL465/603 - Machine Learning

CSL465/603 - Machine Learning CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am

More information

The enabling role of decision support systems in organizational learning

The enabling role of decision support systems in organizational learning Decision Support Systems 32 (2002) 297 309 www.elsevier.com/locate/dsw The enabling role of decision support systems in organizational learning Ganesh Datt Bhatt, Jigish Zaveri* Department of Information

More information

Lecture 1: Basic Concepts of Machine Learning

Lecture 1: Basic Concepts of Machine Learning Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010

More information

A Reinforcement Learning Variant for Control Scheduling

A Reinforcement Learning Variant for Control Scheduling A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement

More information

On the Combined Behavior of Autonomous Resource Management Agents

On the Combined Behavior of Autonomous Resource Management Agents On the Combined Behavior of Autonomous Resource Management Agents Siri Fagernes 1 and Alva L. Couch 2 1 Faculty of Engineering Oslo University College Oslo, Norway siri.fagernes@iu.hio.no 2 Computer Science

More information

Chapter 2 Rule Learning in a Nutshell

Chapter 2 Rule Learning in a Nutshell Chapter 2 Rule Learning in a Nutshell This chapter gives a brief overview of inductive rule learning and may therefore serve as a guide through the rest of the book. Later chapters will expand upon the

More information

FF+FPG: Guiding a Policy-Gradient Planner

FF+FPG: Guiding a Policy-Gradient Planner FF+FPG: Guiding a Policy-Gradient Planner Olivier Buffet LAAS-CNRS University of Toulouse Toulouse, France firstname.lastname@laas.fr Douglas Aberdeen National ICT australia & The Australian National University

More information

SARDNET: A Self-Organizing Feature Map for Sequences

SARDNET: A Self-Organizing Feature Map for Sequences SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

BMBF Project ROBUKOM: Robust Communication Networks

BMBF Project ROBUKOM: Robust Communication Networks BMBF Project ROBUKOM: Robust Communication Networks Arie M.C.A. Koster Christoph Helmberg Andreas Bley Martin Grötschel Thomas Bauschert supported by BMBF grant 03MS616A: ROBUKOM Robust Communication Networks,

More information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

Learning Prospective Robot Behavior

Learning Prospective Robot Behavior Learning Prospective Robot Behavior Shichao Ou and Rod Grupen Laboratory for Perceptual Robotics Computer Science Department University of Massachusetts Amherst {chao,grupen}@cs.umass.edu Abstract This

More information

Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

More information

Analysis of Enzyme Kinetic Data

Analysis of Enzyme Kinetic Data Analysis of Enzyme Kinetic Data To Marilú Analysis of Enzyme Kinetic Data ATHEL CORNISH-BOWDEN Directeur de Recherche Émérite, Centre National de la Recherche Scientifique, Marseilles OXFORD UNIVERSITY

More information

A Stochastic Model for the Vocabulary Explosion

A Stochastic Model for the Vocabulary Explosion Words Known A Stochastic Model for the Vocabulary Explosion Colleen C. Mitchell (colleen-mitchell@uiowa.edu) Department of Mathematics, 225E MLH Iowa City, IA 52242 USA Bob McMurray (bob-mcmurray@uiowa.edu)

More information

INPE São José dos Campos

INPE São José dos Campos INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA

More information

Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification

Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Tomi Kinnunen and Ismo Kärkkäinen University of Joensuu, Department of Computer Science, P.O. Box 111, 80101 JOENSUU,

More information

Dinesh K. Sharma, Ph.D. Department of Management School of Business and Economics Fayetteville State University

Dinesh K. Sharma, Ph.D. Department of Management School of Business and Economics Fayetteville State University Department of Management School of Business and Economics Fayetteville State University EDUCATION Doctor of Philosophy, Devi Ahilya University, Indore, India (2013) Area of Specialization: Management:

More information

Artificial Neural Networks written examination

Artificial Neural Networks written examination 1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14

More information

Learning and Transferring Relational Instance-Based Policies

Learning and Transferring Relational Instance-Based Policies Learning and Transferring Relational Instance-Based Policies Rocío García-Durán, Fernando Fernández y Daniel Borrajo Universidad Carlos III de Madrid Avda de la Universidad 30, 28911-Leganés (Madrid),

More information

TABLE OF CONTENTS TABLE OF CONTENTS COVER PAGE HALAMAN PENGESAHAN PERNYATAAN NASKAH SOAL TUGAS AKHIR ACKNOWLEDGEMENT FOREWORD

TABLE OF CONTENTS TABLE OF CONTENTS COVER PAGE HALAMAN PENGESAHAN PERNYATAAN NASKAH SOAL TUGAS AKHIR ACKNOWLEDGEMENT FOREWORD TABLE OF CONTENTS TABLE OF CONTENTS COVER PAGE HALAMAN PENGESAHAN PERNYATAAN NASKAH SOAL TUGAS AKHIR ACKNOWLEDGEMENT FOREWORD TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES LIST OF APPENDICES LIST OF

More information

XXII BrainStorming Day

XXII BrainStorming Day UNIVERSITA DEGLI STUDI DI CATANIA FACOLTA DI INGEGNERIA PhD course in Electronics, Automation and Control of Complex Systems - XXV Cycle DIPARTIMENTO DI INGEGNERIA ELETTRICA ELETTRONICA E INFORMATICA XXII

More information

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad

More information

A theoretic and practical framework for scheduling in a stochastic environment

A theoretic and practical framework for scheduling in a stochastic environment J Sched (2009) 12: 315 344 DOI 10.1007/s10951-008-0080-x A theoretic and practical framework for scheduling in a stochastic environment Julien Bidot Thierry Vidal Philippe Laborie J. Christopher Beck Received:

More information

Modeling user preferences and norms in context-aware systems

Modeling user preferences and norms in context-aware systems Modeling user preferences and norms in context-aware systems Jonas Nilsson, Cecilia Lindmark Jonas Nilsson, Cecilia Lindmark VT 2016 Bachelor's thesis for Computer Science, 15 hp Supervisor: Juan Carlos

More information

The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma

The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma International Journal of Computer Applications (975 8887) The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma Gilbert M.

More information

Software Maintenance

Software Maintenance 1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories

More information

Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design

Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design Paper #3 Five Q-to-survey approaches: did they work? Job van Exel

More information

Multimedia Application Effective Support of Education

Multimedia Application Effective Support of Education Multimedia Application Effective Support of Education Eva Milková Faculty of Science, University od Hradec Králové, Hradec Králové, Czech Republic eva.mikova@uhk.cz Abstract Multimedia applications have

More information

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these

More information

An empirical study of learning speed in backpropagation

An empirical study of learning speed in backpropagation Carnegie Mellon University Research Showcase @ CMU Computer Science Department School of Computer Science 1988 An empirical study of learning speed in backpropagation networks Scott E. Fahlman Carnegie

More information

On-the-Fly Customization of Automated Essay Scoring

On-the-Fly Customization of Automated Essay Scoring Research Report On-the-Fly Customization of Automated Essay Scoring Yigal Attali Research & Development December 2007 RR-07-42 On-the-Fly Customization of Automated Essay Scoring Yigal Attali ETS, Princeton,

More information

Improving Action Selection in MDP s via Knowledge Transfer

Improving Action Selection in MDP s via Knowledge Transfer In Proc. 20th National Conference on Artificial Intelligence (AAAI-05), July 9 13, 2005, Pittsburgh, USA. Improving Action Selection in MDP s via Knowledge Transfer Alexander A. Sherstov and Peter Stone

More information

SETTING STANDARDS FOR CRITERION- REFERENCED MEASUREMENT

SETTING STANDARDS FOR CRITERION- REFERENCED MEASUREMENT SETTING STANDARDS FOR CRITERION- REFERENCED MEASUREMENT By: Dr. MAHMOUD M. GHANDOUR QATAR UNIVERSITY Improving human resources is the responsibility of the educational system in many societies. The outputs

More information

Emergency Management Games and Test Case Utility:

Emergency Management Games and Test Case Utility: IST Project N 027568 IRRIIS Project Rome Workshop, 18-19 October 2006 Emergency Management Games and Test Case Utility: a Synthetic Methodological Socio-Cognitive Perspective Adam Maria Gadomski, ENEA

More information

Learning to Schedule Straight-Line Code

Learning to Schedule Straight-Line Code Learning to Schedule Straight-Line Code Eliot Moss, Paul Utgoff, John Cavazos Doina Precup, Darko Stefanović Dept. of Comp. Sci., Univ. of Mass. Amherst, MA 01003 Carla Brodley, David Scheeff Sch. of Elec.

More information

An Introduction to Simio for Beginners

An Introduction to Simio for Beginners An Introduction to Simio for Beginners C. Dennis Pegden, Ph.D. This white paper is intended to introduce Simio to a user new to simulation. It is intended for the manufacturing engineer, hospital quality

More information

Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing D. Indhumathi Research Scholar Department of Information Technology

More information

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1 Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial

More information

While you are waiting... socrative.com, room number SIMLANG2016

While you are waiting... socrative.com, room number SIMLANG2016 While you are waiting... socrative.com, room number SIMLANG2016 Simulating Language Lecture 4: When will optimal signalling evolve? Simon Kirby simon@ling.ed.ac.uk T H E U N I V E R S I T Y O H F R G E

More information

Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I

Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I Session 1793 Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I John Greco, Ph.D. Department of Electrical and Computer Engineering Lafayette College Easton, PA 18042 Abstract

More information

A Pipelined Approach for Iterative Software Process Model

A Pipelined Approach for Iterative Software Process Model A Pipelined Approach for Iterative Software Process Model Ms.Prasanthi E R, Ms.Aparna Rathi, Ms.Vardhani J P, Mr.Vivek Krishna Electronics and Radar Development Establishment C V Raman Nagar, Bangalore-560093,

More information

A GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING

A GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING A GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING Yong Sun, a * Colin Fidge b and Lin Ma a a CRC for Integrated Engineering Asset Management, School of Engineering Systems, Queensland

More information

AQUA: An Ontology-Driven Question Answering System

AQUA: An Ontology-Driven Question Answering System AQUA: An Ontology-Driven Question Answering System Maria Vargas-Vera, Enrico Motta and John Domingue Knowledge Media Institute (KMI) The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom.

More information

The dilemma of Saussurean communication

The dilemma of Saussurean communication ELSEVIER BioSystems 37 (1996) 31-38 The dilemma of Saussurean communication Michael Oliphant Deparlment of Cognitive Science, University of California, San Diego, CA, USA Abstract A Saussurean communication

More information

Knowledge-Based - Systems

Knowledge-Based - Systems Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University

More information

*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN

*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN From: AAAI Technical Report WS-98-08. Compilation copyright 1998, AAAI (www.aaai.org). All rights reserved. Recommender Systems: A GroupLens Perspective Joseph A. Konstan *t, John Riedl *t, AI Borchers,

More information

A Version Space Approach to Learning Context-free Grammars

A Version Space Approach to Learning Context-free Grammars Machine Learning 2: 39~74, 1987 1987 Kluwer Academic Publishers, Boston - Manufactured in The Netherlands A Version Space Approach to Learning Context-free Grammars KURT VANLEHN (VANLEHN@A.PSY.CMU.EDU)

More information

A Case Study: News Classification Based on Term Frequency

A Case Study: News Classification Based on Term Frequency A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center

More information

COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS

COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS L. Descalço 1, Paula Carvalho 1, J.P. Cruz 1, Paula Oliveira 1, Dina Seabra 2 1 Departamento de Matemática, Universidade de Aveiro (PORTUGAL)

More information

Mathematics subject curriculum

Mathematics subject curriculum Mathematics subject curriculum Dette er ei omsetjing av den fastsette læreplanteksten. Læreplanen er fastsett på Nynorsk Established as a Regulation by the Ministry of Education and Research on 24 June

More information

Action Models and their Induction

Action Models and their Induction Action Models and their Induction Michal Čertický, Comenius University, Bratislava certicky@fmph.uniba.sk March 5, 2013 Abstract By action model, we understand any logic-based representation of effects

More information

COMPUTER-AIDED DESIGN TOOLS THAT ADAPT

COMPUTER-AIDED DESIGN TOOLS THAT ADAPT COMPUTER-AIDED DESIGN TOOLS THAT ADAPT WEI PENG CSIRO ICT Centre, Australia and JOHN S GERO Krasnow Institute for Advanced Study, USA 1. Introduction Abstract. This paper describes an approach that enables

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

More information

TD(λ) and Q-Learning Based Ludo Players

TD(λ) and Q-Learning Based Ludo Players TD(λ) and Q-Learning Based Ludo Players Majed Alhajry, Faisal Alvi, Member, IEEE and Moataz Ahmed Abstract Reinforcement learning is a popular machine learning technique whose inherent self-learning ability

More information