Artificial Intelligence and Knowledge Based Systems Fall 2009 Frank Hadlock

Definitions of AI The study of representation and search through which intelligent activity can be enacted on a mechanical device. The study of problems at which human beings are currently more adept than computers at solving and the translation and improvement of human solutions into forms which can be implemented on a computer.

Knowledge Based Systems Examples –Expert Systems, Semantic Databases, Tutoring Systems Distinguishing Feature –Structured so that there is a separation between knowledge base (rules for inferring new knowledge), inference engine (applying rules to existing knowledge), and current knowledge.

Physical symbol system hypothesis The physical symbol system hypothesis (PSSH), first formulated by Newell and Simon in their Turing Award paper,1 states that “a physical symbol system [such as a digital computer, for example] has the necessary and sufficient means for intelligent action.” The hypothesis implies that computers, when we provide them with the appropriate symbol-processing programs, will be capable of intelligent action. It also implies, as Newell and Simon wrote, that “the symbolic behavior of man arises because he has the characteristics of a physical symbol system.”

History –Graph theory & state space representation (Euler) –Boolean algebra – propositional calculus (Boole) –Predicate calculus – (Frege) –Descartes Discourse –Turing’s Test –Physical Symbol System Hypothesis –Connectionism

Discourse - Descartes If there were machines which bore a resemblance to our bodies and imitated our actions as closely as possible for all practical purposes, we should still have two very certain means of recognizing that they were not real men. The first is that they could never use words, or put together signs, as we do in order to declare our thoughts to others. For we can certainly conceive of a machine so constructed that it utters words, and even utters words that correspond to bodily actions causing a change in its organs. … But it is not conceivable that such a machine should produce different arrangements of words so as to give an appropriately meaningful answer to whatever is said in its presence, as the dullest of men can do. Secondly, even though some machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal that they are acting not from understanding, but only from the disposition of their organs. For whereas reason is a universal instrument, which can be used in all kinds of situations, these organs need some particular action; hence it is for all practical purposes impossible for a machine to have enough different organs to make it act in all the contingencies of life in the way in which our reason makes us act. (Translation by Robert Stoothoff)

Turing Test The Turing test is a proposal for a test of a machine's ability to demonstrate intelligence. Described by Alan Turing in the 1950 paper "Computing Machinery and Intelligence," it proceeds as follows: a human judge engages in a natural language conversation with one human and one machine, each of which try to appear human; if the judge cannot reliably tell which is which, then the machine is said to pass the test. In order to test the machine's intelligence rather than its ability to render words into audio, the conversation is limited to a text-only channel such as a computer keyboard and screen (Turing originally suggested a teletype machine, one of the few text-only communication systems available in 1950).machineAlan Turing1950Computing Machinery and Intelligenceteletype machine

Knowledge Based Systems Application Areas Game Playing Automated Reasoning Expert Systems Natural Language Understanding Planning and Robotics Computer Based Tutoring Machine Learning Intelligent Agents

Board games can be represented by a usually large but finite set of board configurations or states. The squares of Tic Tac Toe can be numbered 1..9 and each configuration as a sequence over {H,C,B} where many of the 3 9 cannot occur because of order of play. A state BCBBBBBBB may be followed by any of eight states obtained by replacing any of the eight Bs by an H. BBBBBBBBB is the initial state and any state with a row or column or diagonal consisting of all Cs is a winning state for the Computer. If the computer can find a path from start to winning state, the path corresponds to a win for the computer and finding such a path constitutes an example of artificial intelligence. Game Playing and State Space Search

State space search Represented by a four-tuple [N,A,S,GD], where: N is the problem space A is the set of arcs (or links) between nodes. These correspond to the operators. S is a nonempty subset of N. It represents the start state(s) of the problem.

State Space Search continued GD is a nonempty subset of N. It represents the goal state(s) of the problem. The states in GD are described using either: a measurable property of the states a property of the path developed in the search (a solution path is a path from node S to a node in GD )

The 8-puzzle problem as state space search states: possible board positions operators: one for sliding each square in each of four directions, or, better, one for moving the blank square in each of four directions initial state: some given board position goal state: some given board position Note: the “solution” is not interesting here, we need the path.

Eight Puzzle

State space of the 8-puzzle generated by “move blank” operations

Traveling salesperson problem as state space search The salesperson has n cities to visit and must then return home. Find the shortest path to travel. state space: operators: initial state: goal state:

Automated Reasoning and Theorem Proving Logic systems began with Propositional Calculus in which declarative statements with a truth value of true or false are represented by P,Q,R, etc and combined with logic operators Or, And, Not, If. A sentence such as “Bill must take CSC 2020” is represented by letter P and is true or false. Propositional Calculus was extended to Predicate Calculus by adding Predicates (relations), variables, and quantifiers (For All and There Exists). A sentence such as “Every CS major must take CSC 2020” is represented by “(For All X)( CSMajor(X)  MustTake( CSC2020 ))” Given some facts expressed in either Propositional or Predicate Calculus, new facts or knowledge is inferred by inference rules such as modus ponens or resolution. If the computer can find a path from given facts to a new theorem, the path corresponds to a proof and finding such a path constitutes an example of artificial intelligence

Propositional Logic A declarative statement such as “Bill is a CS student” has a truth value of T or F and is denoted by P (a truth variable) Propositions may be combined with logical operators and the composite statement has value as shown below. –P  Q is true if either P or Q are true and false if both are false –P  Q is true if both P and Q are true and false if either is false. –¬ P is true if P is false and false if P is true –P  Q is true if P and Q have the same truth value and false if their values differ –P  Q is false if P is true and Q is false and true otherwise. A tautology is always true. –P  Q  ¬ P  Q is a tautology. –P  (Q  R)  (P  Q)  (P  R) is a tautology.

Rules of Inference P, P  Q then Q - modus ponens ¬ Q, P  Q then ¬ P - modus tollens