Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply this framework to two different representations of learned knowledge, namely control rules and macro-operators, and prove theorems that identify sufficient conditions for learning in each representation. Our proofs are constructive in that they are accompanied with learning algorithms. Our framework captures both empirical and explanation-based speedup learning in a unified fashion. We illustrate our framework with implementations in two domains: symbolic integration and Eight Puzzle. This work integrates many strands of experimental and theoretical work in machine learning, including empirical learning of control rules, macro-operator learning, Explanation-Based Learning (EBL), and Probably Approximately Correct (PAC) Learning.

An important problem in geometric reasoning is to find the configuration of a collection of geometric bodies so as to satisfy a set of given constraints. Recently, it has been suggested that this problem can be solved efficiently by symbolically reasoning about geometry. This approach, called degrees of freedom analysis, employs a set of specialized routines called plan fragments that specify how to change the configuration of a set of bodies to satisfy a new constraint while preserving existing constraints. A potential drawback, which limits the scalability of this approach, is concerned with the difficulty of writing plan fragments. In this paper we address this limitation by showing how these plan fragments can be automatically synthesized using first principles about geometric bodies, actions, and topology.

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.

Termination of logic programs with negated body atoms (here called general logic programs) is an important topic. One reason is that many computational mechanisms used to process negated atoms, like Clark's negation as failure and Chan's constructive negation, are based on termination conditions. This paper introduces a methodology for proving termination of general logic programs w.r.t. the Prolog selection rule. The idea is to distinguish parts of the program depending on whether or not their termination depends on the selection rule. To this end, the notions of low-, weakly up-, and up-acceptable program are introduced. We use these notions to develop a methodology for proving termination of general logic programs, and show how interesting problems in non-monotonic reasoning can be formalized and implemented by means of terminating general logic programs.

The 1995 Robot Competition and Exhibition was held in Montreal, Canada, in conjunction with the 1995 International Joint Conference on Artificial Intelligence. The competition was designed to demonstrate state-of-the-art autonomous mobile robots, highlighting such tasks as goal-directed navigation, feature detection, object recognition, identification, and physical manipulation as well as effective human-robot communication. The competition consisted of two separate events: (1) Office Delivery and (2) Office Cleanup. The exhibition also consisted of two events: (1) demonstrations of robotics research that was not related to the contest and (2) robotics focused on aiding people who are mobility impaired. There was also a Robotics Forum for technical exchange of information between robotics researchers. Thus, this year's events covered the gamut of robotics research, from discussions of control strategies to demonstrations of useful prototype application systems.

CAIR-2 from the Korea Advanced Institute of Science and Technology (KAIST) placed first in the Office Delivery event at the 1995 Robot Competition and Exhibition, held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95). CAIR-2 is a totally self-contained and autonomous mobile robot, and its control architecture incorporates both behavior-based and planner-based approaches. In this article, we present a short description of CAIR-2's hardware, system and control architecture, realtime vision, and speech recognizer.

New network found in any other venue. It is my Collaborative Enterprises (WETICE working groups. John R. Callahan is an assistant professor of computer science in the Department of He received his Ph.D. from the

The 1994 Workshop on Case-Based Reasoning (CBR) focused on the evaluation of CBR theories, models, systems, and system components. The CBR community addressed the evaluation of theories and implemented systems, with the consensus that a balance between novel innovations and evaluations could maximize progress.

The goal of the Workshop on Adaptation and Learning in Multiagent Systems was to focus on research that addresses unique requirements for agents learning and adapting to work in the presence of other agents. Recognizing the applicability and limitations of current machine-learning research as applied to multiagent problems and developing new learning and adaptation mechanisms particularly targeted to this class of problems were the primary research issues that we wanted the authors to address. This article outlines the presentations that were made at the workshop and the success of the workshop in meeting the established goals. Issues that need to be better understood are also presented.

LOLA won the Office Cleanup event at the 1995 Robot Competition and Exhibition, held as part of the Fourteenth International Conference on Artificial Intelligence. The event called for a robot to pick up trash in an unstructured environment and sort it such that the recyclable trash winded up in the recycle bin and the regular trash in the trash bin. The only allowable information lola was given beforehand were model-based descriptions of the trash and recyclables, which it located using color vision. Much of LOLA's success can be attributed to the simple, fast algorithms and methods that also model sensor uncertainty. The ideas and design philosophy that went into LOLA borrow heavily from those of previous competitors' to which we are greatly indebted. These methods and ideas are discussed here.