Chapter 1 Introduction

General Concepts The field of Artificial Intelligence attempts to understand, model, and simulate the behavior (to some extend) of intelligent entities. Artificial Intelligence encompasses areas such as perception, reasoning, planning, and theorem proving. Artificial Intelligence is the study of ideas that enables computers to act intelligently.

Understanding Intelligence The perspective of AI complements the traditional perspectives of psychology, linguistics, and philosophy. –Computer metaphors aid thinking –Computer models force precision –Computer implementations quantify task requirements –Computer programs exhibit unlimited patience

AI Definition Categories The definitions for A.I fall into four categories: 1.Systems act like humans 2.Systems that act rationally 3.Systems that are concerned with thought processes and reasoning 4.Systems that re concerned with behavior

Acting Humanly The Turing Test approach: System intelligence is achieved when a computer is interrogated by a human by teletype, and the human can not tell if there is a computer or a human at the other end. System capabilities needed to pass the Turing Test: –Natural language processing –Knowledge representation –Automated reasoning –Machine learning –Computer vision –Robotics

Thinking Humanly Bringing together computer models from Artificial Intelligence and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind.

Thinking Rationally Patterns for argument structures that always give correct conclusions given correct premises. These patterns were thought that govern the operation of the mind, and initiated the field of logic. For many years dominated the area of A.I Issues: –Uncertain knowledge –Intractable problems

Acting Rationally Acting rationally means acting so as to achieve one’s goals, given one’s beliefs. An agent is just something that perceives and acts In the laws of thought approach to AI, the whole emphasis is on correct inferences. However, this is only part of rational behavior. We need the ability to represent knowledge and reason with it because it enables us to reach good decisions in a wide variety of situations.

Rational Agents The study of AI as rational agent design has two advantages –It is more general than the “laws of thought” because correct inference is only a useful mechanism for achieving rationality, and not a necessary one. –It is more amenable to scientific development that approaches based on human behavior or human thought

Example – Simple Reflex Agent function SIMPLE_REFLEX_AGENT(percept): returns action static: rules, a set of condition action rules state = INTERPRET_INPUT(percept) rule = RULE_MATCH(state, rules) action = RULE_ACTION(rule) return action; end

Intelligent Agents An agent is perceiving its environment through sensors and acting upon that environment through effectors. A Rational Agent is one that does the {\it right} thing. A right action is the one that will cause agent to be most successful. The problem becomes how and when to evaluate agent's success. Performance measure of how –The criteria that determine how successful an agent is When to evaluate –Measure of performance over a long run Issue: Rationality vs. Omniscience. An onmiscient agent knows the actual outcome of its actions and can act accordingly (impossible in reality). Rationality: Expected result given what has been perceived

Intelligent Agents In summary, what is rational at any given time depends on four things: 1.The performance measure that defines degree of success 2.Everything that the agent has perceived so far. We will call this complete perceptual history, the {\bf percept sequence}. 3.What the agent knows about the environment. 4.The actions that the agent can perform Ideal Rational Agent: For each possible percept sequence,an ideal rational agent should do whatever action is expected to maximize its performance measure, on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has Ideal mapping: Percept sequence  actions Possible to describe an agent with a table of actions that the agent does in response to each percept sequence Possible to try out all possible sequences and observe agent's action response Possible to define a specification without an exhaustive enumeration

Intelligent Agents Agent lacks autonomy if actions based on solely in built-in knowledge, not in percepts. System is autonomous to the extent that its behaviour is determined by its own experience. It is not realistic to expect complete autonomy from very start The structure of intelligent agents Agent = Architecture + Program Architecture –Makes percepts available to program – Runs the program –Passes program's actions to effectors as they are generated Agent Programs Table Driven Agents Simple Reflex Agents Reflex Agents with Internal State Goal-based Agents Utility-based Agents

Application Areas In business, computers can suggest financial strategies, and give marketing advice In engineering, computers can check design rules, recall relevant precedent designs, offer design suggestions In manufacturing, computers can perform dangerous, or labor intensive tasks In farming, computers can help in selectively harvest crops, and prune trees In mining, computers can suggest exploration sites, and perform work in hostile environments for humans. In schools, computers can understand students’ mistakes and act as superbooks In hospitals, computers can help in diagnosis, medical imaging, and administering therapies In household, computers can help in planning, and controlling devices

The Foundations of AI Philosophy (428 B.C. – present) –Socrates, Plato, Aristotle (laws for governing the rational part of the mind) –Rene Descartes (dualism) –Wilhelm Leibniz (materialism) –Francis Bacon (empiricist movement) –Dave Hume (induction) –Bertrand Russel (logical positivism) –Aristotle – Newell Simon (means-ends analysis) GPS

The Foundations of AI Mathematics (800 – present) –Al-Khowarazmi (algorithms, notation) –Boole (logic algebras) –Hilbert (limits to proof procedures) –Godel (incompleteness theorem) –Dantig, Edmonds (reduction) –Cook (Computability, NP completeness) –Von Neuman (decision theory)

The Foundations of AI Psychology (1879 – Present) Computer Engineering (1940 – Present) Linguistics (1957 – Present)

State of the Art Technologies –Knowledge based systems –Hidden Markov Models –Belief Networks –Neural Networks Applications –Diagnosis –Medical imaging –Speech recognition –Exploration –Planning