CS 3030 Artificial Intelligence Review for Exam 1

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1 Part of this document is from the lecture notes of Artificial Intelligence Illuminated. Use this review together with your lecture notes, textbook and quizzes to prepare for the exam. 1. Introduction What Is Artificial Intelligence? One definition of Artificial Intelligence is: Artificial intelligence is the study of systems that act in a way that to any observer would appear to be intelligent. However, this definition does not cover the entire study of Artificial Intelligence. A definition that does is as follows: Artificial Intelligence involves using methods based on the intelligent behavior of humans and other animals to solve complex problems. There are two views regarding AI: strong AI and weak AI. According to the strong AI view, it is possible to create a computer that can literally think and is conscience in the same way that a human is conscious. Weak AI is simply the view that intelligent behavior can be modeled and used by computers to solve complex problems. According to the weak AI view, just because a computer behaves intelligently does not prove that it is actually intelligent in the way that a human is. Several experiments and tests were proposed during the debates of strong AI or weak AI: Turing test, Chinese room experiment and halting problem are the most famous ones. You should understand each of them and why they are used to support one side of the debate. Strong Methods and Weak Methods In Artificial Intelligence, two different types of problem solving strategies strong methods and weak methods exist. Weak methods in Artificial Intelligence use systems such as logic, automated reasoning, and other general structures that can be applied to a wide range of problems but that do not necessarily incorporate any real knowledge about the world of the problem that is being solved. In contrast, strong method problem solving depends on a system being given a great deal of knowledge about its world and the problems that it might encounter. From Aristotle to Babbage The field of Artificial Intelligence could not exist without the contributions made by the philosophers, mathematicians, and logicians of the past. Before the 20th century, Aristotle, Peter Abelard, Gottfried Leibniz, George Boole, and Charles Babbage made important contributions to the field of Artificial Intelligence. Alan Turing and the 1950s

2 In 1950, Alan Turing published a paper called Computing Machinery & Intelligence, which was one of the first papers to be written on Artificial Intelligence. He also designed the Turing test, which can be used to judge the success or otherwise of an attempt to produce a thinking computer. The term Artificial Intelligence was first used by John McCarthy in 1956 at a conference in Dartmouth College, in Hanover, New Hampshire. In 1958, McCarthy invented the LISP programming language, which is still widely used today in Artificial Intelligence research. In 1957, Newell and Simon invented the General Problem Solver (GPS), whose purpose was to solve almost any logical problem. However, the GPS could not be used to solve all kinds of problems. During this period, there was a great deal of optimism about Artificial Intelligence. The 1960s to the 1990s After the 1950s, a great deal of the optimism about Artificial Intelligence was replaced with a sense of realism. The aim of the study of Artificial Intelligence is no longer to create a robot as intelligent as a human, but rather to use algorithms, heuristics, and methodologies based on the ways in which the human brain solves problems. The Chinese Room The Chinese Room argument was proposed by the American philosopher John Searle against strong AI. This argument attempts to show that being able to run a computer program that behaves in an intelligent way does not necessarily produce understanding, consciousness, or real intelligence. This argument contrasts with Turing s view that a computer system that could fool a human into thinking it was human too would actually be intelligent. AI in the 21st Century Many forms of Artificial Intelligence are being used today. Artificial Intelligence is likely to become more and more prevalent in our societies in the future. Some examples of Artificial Intelligence include devices such as washing machines, cars, and elevator control mechanisms using fuzzy logic; intelligent agents, such as those used by Internet search engines; robots used in science, law enforcement, etc.; and expert systems used in the medical field. Overview of difference AI topics that will be covered in this course Major AI topics include knowledge representation, state-space search, learning, and many more. We will introduce three knowledge representation methods. Semantic net: nodes and edges. It is used for state-space search. Search space and search tree are both semantic nets. Propositional logic: P -> Q, P - Q

3 Predicate logic: x ( Student(x) Smart(x) ) Expert systems emulates the decision-making ability of a human expert. This is usually a rule based approach and we use logic to represent rules. The most important (first) step to solve a problem by searching or learning is modeling. We will summary details about searching in the next section. There are three types of machine learning: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: no labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end. Reinforcement learning: a computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent. 2. Lisp Two types of AI programming languages Prolog is basically a database of facts and rules. Lisp handles lists of data, where everything is considered as a list, even programs. This means we can write self-modifying programs in Lisp. One major reason why these two languages are preferred for AI research is that there is a direct mapping between programs and math formulas. In Racket (a dialect of Lisp), you should know how to: Represent things: number, character, string, symbol, list, structure, variable, expression. Define and call a function Local binding using let Branch using if and cond Looping using recursion Construct and manipulate lists and structures Use high-order functions apply, map and filter and use lambda to facilitate this usage 3. State-Space Search Search Spaces

4 A search space is a representation of the set of possible choices in a given problem, one or more of which are the solution to the problem. It consists of a set of states, connected by paths that represent actions. Search spaces are also known as state spaces. Many search problems in Artificial Intelligence can be represented by a state space. Semantic Trees A semantic tree is a kind of semantic net that has the following properties: Each node (except the root node) has exactly one predecessor and one or more successors. One node, called the root node, has no predecessors. Some nodes, called leaf nodes, have no successors. Apart from leaf nodes, all nodes have one or more successors. Apart from the root node, all nodes have exactly one predecessor. An ancestor of a node is a node further up the tree in some path. A descendent comes after a node in a path in the tree. Search Trees The possible paths through a semantic net can be represented as a search tree, which is a type of semantic tree. Each node in a search tree represents a path, with successive layers in the tree representing longer and longer paths. Goal Trees A goal tree is a form of semantic tree that is used to represent problems. The solution to the problem being represented is called the goal, and each individual step along the way is a subgoal. Each node in a goal tree represents a subgoal, and that node s children are the subgoals of that goal. Problem Solving as Search A problem can be represented through a search space. It can be considered to consist of a goal and a set of actions that can be taken to lead to the goal. Methods for examining a search space are called search methods. Data-Driven or Goal-Driven Search The two main approaches to searching a search tree are data-driven search and goal-driven search. Data-driven search, also known as forward chaining, starts from an initial state and uses actions that are allowed to move forward until a goal is reached. Goal-driven search, also known as backward chaining, starts at the goal and works back toward a start state, by seeing what moves could have led to the goal state. Properties of Search Methods Search methods should have certain properties to be useful: Complexity: It is useful to discuss how efficient a search method is, in terms of both time and space. The time complexity of a method is related to the length of time that the method would

5 take to find a goal state. The space complexity is related to the amount of memory that the method needs to use. Completeness: A search method that is complete is guaranteed to find a goal state if one exists. Completeness is usually a desirable property because running a search method that never finds a solution is not often helpful. Optimality: A search method is optimal if it is guaranteed to find the best solution that exists. Such a search method will find the path to a goal state that involves taking the least number of steps. Irrevocability: Methods that do not use backtracking, and which therefore examine just one path, are described as irrevocable. Irrevocable search methods will often find suboptimal solutions to problems because they tend to be fooled by solutions that look good locally but are less favorable when compared with other solutions elsewhere in the search space. Generate and Test (Uninformed Search) Generate and Test is the simplest form of brute-force search (also called exhaustive search). It involves generating each node in the search space and testing it to see if it is a goal node. If it is, the search has succeeded and need not carry on. Otherwise, the procedure moves on to the next node. Generate and Test is said to a blind search technique. Depth-First Search Depth-first search is a search algorithm that follows each path to its greatest depth before moving on to the next path. It is an example of brute-force search, or exhaustive search. Depth-first search is often used by computers for search problems such as locating files on a disk, or by search engines for spidering the Internet. Breadth-First Search Breadth-first search involves traversing a tree by breadth rather than by depth. It is used when examining game trees. Depth-First Iterative Deepening Depth-First Iterative Deepening, or DFID (also called Iterative Deepening Search or IDS), is an exhaustive search technique that combines depth-first with breadth-first search. The DFID algorithm involves repeatedly carrying out depth-first searches on the tree, starting with a depth-first search limited to a depth of one, then a depth-first search of depth two, and so on, until a goal node is found. This section examines the characteristics of this technique. BFS vs. DFS vs. IDS BFS DFS IDS Time Complexity O(b d ) O(b m ) O(b m ) Space Complexity O(b d ) O(bm) O(bd) Completeness yes no Yes Optimality yes no yes Irrevocability yes yes yes

6 where b is the branching factor of the tree; d is the depth of the shallowest goal state reached and m is the depth of the deepest goal state reached. Using Heuristics for Search To perform a search, humans use any additional information that they might have to direct the search better. Such information is called a heuristic. Computers can use heuristics too. A heuristic is a rule of thumb that is applied to solve the problem. Informed and Uninformed Methods Search methods that use heuristics are informed, and those that do not are uninformed, or blind. Bestfirst search is an example of informed search, whereas breadth-first and depth-first searches are examples of uninformed or blind search. Three important functions Given a node n in the search space, there are three functions we often use to perform heuristic search: Heuristic Function h(n) = estimated cost of the cheapest path from the state at node n to a goal state Shortest Path Cost Function g(n) = shortest path cost from the initial state to the state at node n. Evaluation Function f(n) = cost estimate used during search h(n) looks forward, while g(n) looks backwards. Choosing a Good Heuristic Choosing the right heuristic can make a significant difference in our ability to solve a problem. The better (more informed) the heuristic is, the fewer nodes it needs to examine in the search tree to find a solution. In general, we prefer an admissible heuristic, which means the heuristic value is less than or equal to the actual cost to the goal state. Best-First Search Best-first search has a simple idea: choose the best state to expand the search tree. The question is how to decide best? This is done by maintaining a priority queue using the evaluation function f(n). If f(n) = g(n), we have uniform-cost search; If f(n) = h(n), we have greedy best-first search; If f(n) = g(n) + h(n), we have either A* search or A search (depending on whether the heuristic is admissible).

7 Real-Time A* Algorithms Real-time A* algorithms is the one we use to implement A* search because then we don t have to calculate g(n) before head. Thus, it improve the efficiency of the method. Uniform-cost vs. Greedy vs. A* Uniform-cost Greedy A* Time Complexity O(b^(# nodes with g(n) < C*)) O(b m ) O(b^(# nodes with f(n) < C*)) Completeness yes no Yes Optimality yes no yes Irrevocability yes yes yes Hill Climbing Hill climbing is an example of an informed search method because it expand to the first successor state that is better than the current node. Better usually means having a smaller h(n) value. Steepest Ascent Hill Climbing improves hill climbing in that it expand to the best successor state. Hill climbing is a greedy local search: grab good neighbor without thinking ahead. One of its problems is that it can get stuck at a local maximum, plateau or a ridge! A possible solution is to random restart or re-model the problem. The difference between Steepest Ascent Hill Climbing and Greedy Best-First Search is that there is no backtracking in hill climbing. Beam Search Beam search is a form of breadth-first search that employs a heuristic. Beam search works using a threshold so that only the best few paths are followed downward at each level. Beam search is thus efficient in memory usage so good for high branching factor search space. However, since it is not an exhaustive search, it may fail to find the goal state.

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