(IKS 2002).

This first part covers mathematical that teachers will find useful.

As a result, they knowledge of an agent (that is, its epistemic coarse-grained level of abstraction, KBwould argue, it is not possible to discuss state) can be characterized as the Ss can be characterized in terms of two the knowledge of a system independently collection of all possible worlds that components: (1) a knowledge base, encoding of the task context in which are consistent with the knowledge the knowledge embodied by the system is meant to operate. I won't held by the agent. If the knowledge of the system, and (2) a reasoning engine, go into too many details here because the agent is complete, then the epistemic which is able to query the knowledge a detailed discussion of the declarative state contains only one world. A base, infer or acquire knowledge from versus the procedural argument is well nice feature of Levesque and Lakemeyer's external sources, and add new knowledge beyond the scope of this review. The treatment of epistemic logic is that to the knowledge base. Levesque important point to make is that in contrast to many other treatments and Lakemeyer's The Logic of Knowledge Levesque and Lakemeyer's approach is of modalities, the discussion is reasonably Bases deals with the "internal logic" of situated in a precise AI research easy to follow for people who are a KBS: It provides a formal account of paradigm, which considers knowledge not experts in the field. This is the result the interaction between a reasoning bases as declaratively specified, task-independent of two main features of this analysis: engine and a knowledge base.

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.

This article presents an overview and survey of current work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with modelbased reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characterizes the types of multimodal reasoning integrations where CBR can play a role, identifies the types of roles that CBR components can fulfill, and provides examples of integrated CBR systems. Past progress, current trends, and issues for future research are discussed.

RoboCup-2001 was the Fifth International RoboCup Competition and Conference. It was held for the first time in the United States, following RoboCup-2000 in Melbourne, Australia; RoboCup-99 in Stockholm; RoboCup-98 in Paris; and RoboCup-97 in Osaka. This article discusses in detail each one of the events at RoboCup-2001, focusing on the competition leagues.

Serving hors d'oeuvres is not as easy as it might For the fifth five entries took on the challenge of devices were connected to both robots. Mannequins creating service robots who can offer hors were mounted on top of each robot to d'oeuvres to attendees of the robot exhibition. The robots communicated area, find and stop at people to offer food and with each other through a local area network interact with them, detect when more food is on wireless network cards on their laptop computers. For example, Ron Nucci from expected responses. The robot had voice-recognition guest, and serves him/her.

Fusions are a simple way of combining logics. For normal modal logics, fusions have been investigated in detail. In particular, it is known that, under certain conditions, decidability transfers from the component logics to their fusion. Though description logics are closely related to modal logics, they are not necessarily normal. In addition, ABox reasoning in description logics is not covered by the results from modal logics. In this paper, we extend the decidability transfer results from normal modal logics to a large class of description logics. To cover different description logics in a uniform way, we introduce abstract description systems, which can be seen as a common generalization of description and modal logics, and show the transfer results in this general setting.

We propose a general Bayesian framework for performing independent (leA) which relies on ensemble learning and linearcomponent analysis response theory known from statistical physics. We apply it to both discrete and continuous sources. For the continuous source the underdetermined (overcomplete) case is studied. The naive mean-field approach fails in this case whereas linear response theory-which gives an improved estimate of covariances-is very efficient. The examples given are for sources without temporal correlations. However, this derivation can easily to treat temporal correlations. Finally, the frameworkbe extended of generating new leA algorithms without needingoffers a simple way to define the prior distribution of the sources explicitly.

The model structure is an abstrac- tion of the hippocampus or the olfactory cortex. We propose a simple generalized Hebbian rule, using temporal-activity-dependent LTP and LTD, to encode both magnitudes and phases of oscillatory patterns into the synapses in the network. After learning, the model responds resonantly to inputs which have been learned (or, for networks which operate essentially linearly, to linear combinations of learned inputs), but negligibly to other input patterns. Encoding both amplitude and phase enhances computational capacity, for which the price is having to learn both the excitatory-to-excitatory and the excitatory-to-inhibitory connections. Our model puts contraints on the form of the learning kernal A(r) that should be experimenally observed, e.g., for small oscillation frequencies, it requires that the overall LTP dominates the overall LTD, but this requirement should be modified if the stored oscillations are of high frequencies.