Artificial Intelligence introduction(2)

CSE 402K3R20/K3R23

AI has roots in a number of scientific disciplinescomputer science and engineering (hardware and software)philosophy (rules of reasoning)mathematics (logic, algorithms, optimization)cognitive science and psychology (modeling high level human/animal thinking)neural science (model low level human/animal brain activity)linguisticsA Brief History of Artificial Intelligence

A Brief History of Artificial IntelligenceThe birth of AI (1943 1956)Pitts and McCulloch (1943): simplified mathematical model of neurons can realize all propositional logic primitives (can compute all Turing computable functions)Allen Turing: Turing machine and Turing test (1950)Claude Shannon: information theory; possibility of chess playing computers

Early enthusiasm (1952 1969)1956 Dartmouth conferenceJohn McCarthy (Lisp);Marvin Minsky (first neural network machine);Emphasize on intelligent general problem solvingLisp (AI programming language);Resolution by John Robinson (basis for automatic theorem proving);heuristic search (A*, AO*, game tree search)A Brief History of Artificial Intelligence

A Brief History of Artificial Intelligence

Emphasis on knowledge (1966 1974)domain specific knowledge is the key to overcome existing difficultiesknowledge representation (KR) paradigms

Knowledge-based systems (1969 1999)DENDRAL: the first knowledge intensive system (determining 3D structures of complex chemical compounds)MYCIN: first rule-based expert system (containing 450 rules for diagnosing blood infectious diseases)EMYCIN: an ES shellPROSPECTOR: first knowledge-based system that made significant profit (geological ES for mineral deposits)

AI became an industry (1980 1989)wide applications in various domainscommercially available toolsCurrent trends (1990 present)more realistic goals more practical (application oriented)distributed AI and intelligent software agentsresurgence of neural networks and emergence of genetic algorithmsA Brief History of Artificial Intelligence

The relational languages like PROLOG [ PROgramming in LOgic] AND LISP [LISt Processing] in AI.LISP is well suited for handling lists, where as PROLOG is designed for logic ProgrammingProgramming languages for AIArchitecture of AI machineAt the early stage of programs of AI, common machine used for conventional programming were also used for AI programming. This special architecture, called LISP and PROLOG machine. Most of this architecture are used in research laboratory, and are not available in the open commercial market.

Possible ApproachesAI tends to work mostly in this area

Think well

Develop formal models of knowledge representation, reasoning, learning memory, problem solving, that can be rendered in algorithms.There is often an emphasis on a systems that are provably correct, and guarantee finding an optimal solution.

Act wellFor a given set of inputs, generate an appropriate output that is not necessarily correct but gets the job done.

A heuristic (heuristic rule, heuristic method) is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits search for solutions in large problem spaces. Heuristics do not guarantee optimal solutions; in fact, they do not guarantee any solution at all: all that can be said for a useful heuristic is that it offers solutions which are good enough most of the time. Feigenbaum and Feldman, 1963, p. 6

Think like humansCognitive science approach Focus not just on behavior and I/O but also look at reasoning process. Computational model should reflect how results were obtained. Provide a new language for expressing cognitive theories and new mechanisms for evaluating themGPS (General Problem Solver): Goal not just to produce humanlike behavior (like ELIZA), but to produce a sequence of steps of the reasoning process that was similar to the steps followed by a person in solving the same task.

Act like humansBehaviorist approach.Not interested in how you get results, just the similarity to what human results are. Exemplified by the Turing Test (Alan Turing, 1950).ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test. Coded at MIT during 1964-1966 by Joel Weizenbaum.

Areas of AI and their inter-dependenciesSearchVisionPlanningMachine LearningKnowledge RepresentationLogicExpert SystemsRoboticsNLP

Branches of AILogical AI Search Natural language processingpattern recognition Knowledge representation Inference From some facts, others can be inferred. Automated reasoning Learning from experience Planning To generate a strategy for achieving some goalEpistemology This is a study of the kinds of knowledge that are required for solving problems in the world. Genetic programmingEmotions???

****