As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).

As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).

The importance of contextual reasoning is emphasized by various researchers in AI. (A partial list includes John McCarthy and his group, R. V. Guha, Yoav Shoham, Giuseppe Attardi and Maria Simi, and Fausto Giunchiglia and his group.) Here, we survey the problem of formalizing context and explore what is needed for an acceptable account of this abstract notion.

Although most scheduling problems are NP-hard, domain specific techniques perform well in practice but are quite expensive to construct. In adaptive problem-solving solving, domain specific knowledge is acquired automatically for a general problem solver with a flexible control architecture. In this approach, a learning system explores a space of possible heuristic methods for one well-suited to the eccentricities of the given domain and problem distribution. In this article, we discuss an application of the approach to scheduling satellite communications. Using problem distributions based on actual mission requirements, our approach identifies strategies that not only decrease the amount of CPU time required to produce schedules, but also increase the percentage of problems that are solvable within computational resource limitations.

The University of Chicago's robot, CHIP, is part of the Animate Agent Project, aimed at understanding the software architecture and knowledge representations needed to build a general-purpose robotic assistant. CHIP's strategy for the Office Cleanup event of the 1995 Robot Competition and Exhibition was to scan an entire area systematically and, as collectible objects were identified, pick them up and deposit them in the nearest appropriate receptacle. This article describes CHIP and its various systems and the ways in which these elements combined to produce an effective entry to the robot competition.

A model of the hippocampus is presented which forms rapid self -organized representations of input arriving via the perforant path, performs recall of previous associations in region CA3, and performs comparison of this recall with afferent input in region CA 1. This comparison drives feedback regulation of cholinergic modulation to set appropriate dynamics for learning of new representations in region CA3 and CA 1. The network responds to novel patterns with increased cholinergic modulation, allowing storage of new self-organized representations, but responds to familiar patterns with a decrease in acetylcholine, allowing recall based on previous representations. This requires selectivity of the cholinergic suppression of synaptic transmission in stratum radiatum of regions CA3 and CAl, which has been demonstrated experimentally. 1 INTRODUCTION A number of models of hippocampal function have been developed (Burgess et aI., 1994; Myers and Gluck, 1994; Touretzky et al., 1994), but remarkably few simulations have addressed hippocampal function within the constraints provided by physiological and anatomical data. Theories of the function of specific subregions of the hippocampal formation often do not address physiological mechanisms for changing dynamics between learning of novel stimuli and recall of familiar stimuli.

A model of the hippocampus is presented which forms rapid self -organized representations of input arriving via the perforant path, performs recall of previous associations in region CA3, and performs comparison of this recall with afferent input in region CA 1. This comparison drives feedback regulation of cholinergic modulation to set appropriate dynamics for learning of new representations in region CA3 and CA 1. The network responds to novel patterns with increased cholinergic modulation, allowing storage of new self-organized representations, but responds to familiar patterns with a decrease in acetylcholine, allowing recall based on previous representations. This requires selectivity of the cholinergic suppression of synaptic transmission in stratum radiatum of regions CA3 and CAl, which has been demonstrated experimentally. 1 INTRODUCTION A number of models of hippocampal function have been developed (Burgess et aI., 1994; Myers and Gluck, 1994; Touretzky et al., 1994), but remarkably few simulations have addressed hippocampal function within the constraints provided by physiological and anatomical data. Theories of the function of specific subregions of the hippocampal formation often do not address physiological mechanisms for changing dynamics between learning of novel stimuli and recall of familiar stimuli.

Four overlapping patterns (1-4) are presented sequentially.

The traditional reliability design methods are imperfect because the designed systems aim at fewer faults, but once a fault happens, the systems might hard fail. To solve this problem, we present a self-maintenance machine (SMM), one that can maintain its functions flexibly even though faults occur. To achieve the capabilities of diagnosing and repair planning, a model-based approach that uses qualitative physics was proposed. Regarding the repair-executing capability, control-type repair strategy was followed. A prototype of the SMM was developed, and it succeeded in maintaining its functions if the structure did not change. However, the prototype revealed the following problems when its reasoning system was used with a commercial product as embedded software: (1) poor performance of the reasoning system, (2) system size that was too large, (3) low adaptability to environmental changes, and (4) roughness of qualitative repair operations. To solve these problems, we proposed new reasoning method based on virtual cases and fuzzy qualitative values. This methodology is one of knowledge compilation, which gives better reasoning performance and can deal with real-world applications such as the SMM. By using this method, we finally developed a commercial photocopier that has self-maintainability and is more robust against faults. The commercial version has been supplied worldwide as a product of Mita Industrial Co., Ltd., since April 1994.

OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.