Approaches to Artificial Intelligence

The field of artificial intelligence (AI) has as its goal the development of machines that can perceive, reason, communicate, and act in complex environments much like humans can, or possibly even better than humans can. Even though the field has produced some practically useful machines with rudiments of these abilities, it is generally conceded that the ultimate goal is still distant. That being so, there is much discussion and argument about what are the best approaches for AI-best in the sense of laying the core foundations for achieving ultimate goals as well as best in the sense of producing practically useful shorter term results. Thus, a number of different paradigms have emerged over the past thirty-five years or so. Each has its ardent advocates, and some have produced sufficiently many interesting results so as not to be dismissable out of hand. Perhaps combinations of these approaches will be required. In any case, the advocates of these approaches often feel that theirs is the ``breakthrough'' methodology that deserves special support.

[1]  Claude E. Shannon,et al.  Programming a computer for playing chess , 1950 .

[2]  Moshe Tennenholtz,et al.  Emergent Conventions in Multi-Agent Systems: Initial Experimental Results and Observations (Preliminary Report) , 1992, KR.

[3]  Jon Doyle,et al.  Preferential Semantics for Goals , 1991, AAAI.

[4]  Sebastian Thrun,et al.  Explanation-Based Neural Network Learning for Robot Control , 1992, NIPS.

[5]  Nils J. Nilsson,et al.  Toward agent programs with circuit semantics , 1992 .

[6]  Hector J. Levesque,et al.  A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.

[7]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[8]  Michael P. Wellman A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems , 1993, J. Artif. Intell. Res..

[9]  Pattie Maes,et al.  Learning Interface Agents , 1993, AAAI.

[10]  Hiroaki Kitano,et al.  Massively Parallel Artificial Intelligence , 1991, IJCAI.

[11]  Joseph Y. Halpern An Analysis of First-Order Logics of Probability , 1989, IJCAI.

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Barbara Hayes-Roth,et al.  A Blackboard Architecture for Control , 1985, Artif. Intell..

[14]  Yoav Shoham,et al.  Agent-Oriented Programming , 1992, Artif. Intell..

[15]  Allen Newell,et al.  Computer science as empirical inquiry: symbols and search , 1976, CACM.

[16]  Stewart W. Wilson The animat path to AI , 1991 .

[17]  E. Feigenbaum,et al.  Computers and Thought , 1963 .