This article is a sequel to an article titled "A New Direction in AI--Toward a Computational Theory of Perceptions," which appeared in the Spring 2001 issue of AI Magazine (volume 22, No. 1, 73-84). The concept of precisiated natural language (PNL) was briefly introduced in that article, and PNL was employed as a basis for computation with perceptions. In what follows, the conceptual structure of PNL is described in greater detail, and PNL's role in knowledge representation, deduction, and concept definition is outlined and illustrated by examples. What should be understood is that PNL is in its initial stages of development and that the exposition that follows is an outline of the basic ideas that underlie PNL rather than a definitive theory. A natural language is basically a system for describing perceptions.

Li is not small compared to that of A. However, To understand how this rule works, let us return to the submarine example and assume that there are two groups of experts El,..., As is pointed out in Zadeh (1979a), the Dempster rule P*(notA) 1. This, in a nutshell, is the basic idea underly-of combination of evidence may lead to counterintuitive coning the Dempster-Shafer theory. The An important observation is in order at this juncture. P(A), that S is in A, the answer would be (after the object under consideration does not exist. P*(A) are the degrees of belief and plausibility associated of evidence, consider the following situation.

This article is an edited transcript of a lecture given at IJCAI-99, Stockholm, Sweden, on 4 August 1999. The article summarizes concepts, principles, and tools that were found useful in applications involving causal modeling. The principles are based on structural-model semantics in which functional (or counterfactual) relationships representing autonomous physical processes are the fundamental building blocks. The article presents the conceptual basis of this semantics, illustrates its application in simple problems, and discusses its ramifications to computational and cognitive problems concerning causation. It is not an easy topic to speak about, but it is a fun topic to speak about.

In fact, we have a knowledge infrastructure already, and it is already immense. AI Mugaztine 7(l): 34- served the most successful work on expert systems: that (today) knowledge comes (mostly) from people. Editor: Mark Stefik Xerox PARC 3333 Coyote Hill Road Palo Alto, California 94304 Workshop on the Foundations of Al: An On-The-Spot Report The NSF and AAAI sponsored Workshop on the Foundations of AI (6-8 February 1986, Las Cruces, New Mexico) is over and, from my perspective at least, it was a very worthwhile event. I am preparing a report that I will send to you in due course. In addition, I noticed that John McCarthy was snapping freely with his camera at the workshop.

Decision theory and nonmonotonic logics are formalisms that can be employed to represent and solve problems of planning under uncertainty. We analyze the usefulness of these two approaches by establishing a simple correspondence between the two formalisms. The analysis indicates that planning using nonmonotonic logic comprises two decision-theoretic concepts: probabilities (degrees of belief in planning hypotheses) and utilities (degrees of preference for planning outcomes). We present and discuss examples of the following lessons from this decision-theoretic view of nonmonotonic reasoning: (1) decision theory and nonmonotonic logics are intended to solve different components of the planning problem; (2) when considered in the context of planning under uncertainty, nonmonotonic logics do not retain the domain-independent characteristics of classical (monotonic) logic; and (3) because certain nonmonotonic programming paradigms (for example, frame-based inheritance, nonmonotonic logics) are inherently problem specific, they might be inappropriate for use in solving certain types of planning problems. We discuss how these conclusions affect several current AI research issues.

The way work load is shared between these stages depends on the particular approach in use. The reader unfamiliar with Petri nets needs only know the following: They are made of places, transitions, and tokens. A place can be seen as a token holder. When it contains one or more tokens, it is said to be marked. Transitions allow tokens to circulate from place to place.

The choice of implication as a representation for empirical associations and for deduction as a mode of inference requires a mechanism extraneous to deduction to manage uncertainty associated with inference. Consequently, the interpretation of representations of uncertainty is unclear. Representativeness, or degree of fit, is proposed as an interpretation of degree of belief for classification tasks. The calculation of representativeness depends on the nature of the associations between evidence and conclusions. Patterns of associations are characterized as endorsements of conclusions.

The American Association for Artificial Intelligence (AAAI) held its 1998 Fall Symposium Series on 23 to 25 October at the Omni Rosen Hotel in Orlando, Florida. This article contains summaries of seven of the symposia that were conducted: (1) Cognitive Robotics; (2) Distributed, Continual Planning; (3) Emotional and Intelligent: The Tangled Knot of Cognition; (4) Integrated Planning for Autonomous Agent Architectures; (5) Planning with Partially Observable Markov Decision Processes; (6) Reasoning with Visual and Diagrammatic Representations; and (7) Robotics and Biology: Developing Connections. Research in cognitive robotics is concerned with the theory and implementation of robots that reason, act, and perceive in changing incompletely known, unpredictable environments. Such robots must have higherlevel cognitive functions that involve, for example, reasoning about goals, actions, the cognitive states of other agents, and time as well as when to perceive and what to look for.

We present a tutorial introduction to the area of preference handling--one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling--representation, reasoning, and elicitation--can be understood. Our intention is not to provide a technical description of the diverse methods used but rather to provide a general perspective on the problem and its varied solutions and to highlight central ideas and techniques. Hence an understanding of the various aspects of preference handling should be of great relevance to anyone attempting to build systems that act on behalf of users or simply support their decisions. This could be a shopping site that attempts to help us identify the most preferred item, an information search and retrieval engine that attempts to provide us with the most preferred pieces of information, or more sophisticated embedded agents such as robots, personal assistants, and so on.

It was started in 2005 and is an annual event that attracts between 5 and 10 teams. It has since been organized by the AI group at Clausthal University of Technology. MAPC is not collocated with any other event. Using our MASSim platform, the participants are running their own systems locally and only interact with the tournament server over the Internet. A steering committee oversees the whole process and determines the organization committee. The scenario changes every other year: the current one is "Agents on Mars." The goal was to implement a team of heterogeneous, cooperating agents to occupy zones on planet Mars. The infrastructure on Mars is given by a directed graph (300 nodes). Agents could take on roles (explorer, sentinel, saboteur, repairer, inspector) and needed to cooperate in an environment with incomplete knowledge so as to win against a competing team: the graph was not known, and each action comes at a price. Conquered terrain brings in money to improve agents. The timeline of the contest is as follows.