Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.

Sensor abuse is a serious and debilitating condition. However, one must remember that it is a disease, not a crime.1 As such, it can be treated. This article presents a case study in sensor abuse. This particular subject was lucky enough to pull himself out of his pitiful condition, but others are not so lucky. The article also describes a 12- step behavior-modification program modeled on this and other successful case studies.

Although they then cite a "slow, rigorous proof" by K. Hornik et al. that implies the superiority of neural systems Put our 27 years experience placing technical In practice, since neural nets and Turing-equivalent systems professionals to work for you. If you earn over $35,000, we have a better, more rewarding job for you... right depend on the arithmetic of real numbers rather now. Call (301) 231-9000 or send your resume in infinite precision fashion. These computations can be confidence to: Dept. The details of what this superiority entails remain unclear.

The name of a or selecting models of a target physical embodied in a model is defined as a target system provides access to a system for a given qualitative reasoning position taken in each of the possible description of the system topology of task. It was motivated by two dimensions. Perspective taking as a the target circuit--a network of circuit observations regarding modeling in process is defined as formulating or components and their connections general and work in qualitative selecting a scenario model of a target by nodes. This information is physics in particular.

The Association for the Advancement of Artificial Intelligence held its 1992 Fall Symposium Series on October 23-25 at the Royal Sonesta Hotel in Cambridge, Massachusetts. This article contains summaries of the five symposia that were conducted: Applications of AI to Real-World Autonomous Mobile Robots, Design from Physical Principles, Intelligent Scientific Computation, Issues in Description Logics: Users Meet Developers, and Probabilistic Approaches to Natural Language.

The first Robotics Exhibition and Competition sponsored by the Association for the Advancement of Artificial Intelligence was held in San Jose, California, on 14-16 July 1992 in conjunction with the Tenth National Conference on AI. This article describes the history behind the competition, the preparations leading to the competition, the threedays during which 12 teams competed in the three events making up the competition, and the prospects for other such competitions in the future.

The camera is mounted on a rotating table that allows it to turn 360 degrees independently of robot motion. Interestingly, the two teams processor (Z80) controls the robot's used vastly different approaches in the design wheel speed and direction. 's software design is hierarchical in The final scores for the robots, based solely structure. At the top level is a supervising on competition-day performance, constitute planning system that decides when to call only a rough evaluation of the merits of the subordinate modules for movement, vision, various systems. This article provides a technical or the recalibration of the robot's position.

My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.

Kluwer. See also: Symbolic Model Checking: An Approach to the State Explosion Problem. Doctoral thesis, Carnegie Mellon University, 1992 (http://www.kenmcmil.com/pubs/thesis.pdf). J.R. Burch, E.M. Clarke, K.L. McMillan, D.L. Dill, L.J. Hwang, Symbolic model checking: 1020 States and beyond, Information and Computation, Volume 98, Issue 2, June 1992, Pages 142-170 (http://www.sciencedirect.com/science/article/pii/089054019290017A). Burch, J. R.; Clarke, E.M.; McMillan, K. L.; Dill, D.L., Sequential circuit verification using symbolic model checking, Design Automation Conference, 1990. Proceedings, 27th ACM/IEEE, vol., no., pp.46,51, 24-28 Jun 1990. (https://ieeexplore.ieee.org/document/114827) Burch, J.R.; Clarke, E.M.; Long, D.E.; McMillan, K.L.; Dill, D.L., Symbolic model checking for sequential circuit verification, Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, vol.13, no.4, pp.401,424, Apr 1994 (https://ieeexplore.ieee.org/document/275352). E. M. Clarke, O. Grumberg, K. L. McMillan, and X. Zhao. 1995. Efficient generation of counterexamples and witnesses in symbolic model checking. In Proceedings of the 32nd annual ACM/IEEE Design Automation Conference (DAC '95). ACM, New York, NY, USA, 427-432 (http://dl.acm.org/citation.cfm?id=217565). Burch, Jerry R.; Clarke, Edmund M.; Long, David E.; McMillan, Kenneth L.; and Dill, David L., Symbolic Model Checking for Sequential Circuit Verification. IEEE Transactions On Computer-Aided Design of Integrated Circuits and Systems, Vol. 13, No. 4, pp. 401-424, April 1994 (http://www.cs.cmu.edu/~emc/papers/Conference%20Papers/Sequential%20circuit%20verification%20using%20symbolic%20model%20checking.pdf).

In Graves, S. C., Zipkin, P. H., and Kan, A. H. G. R. (Eds.), Logistics of Production and Inventory: Handbooks in Operations Research and Management Science, Volume 4, pp. 445–522. North-Holland.