In this paper, we describe KoMeT, a theorem prover for full first order logic. KoMeT is based on the connection method. Our main goal is to develop an adequate proof procedure by integrating a variety of different proof techniques.

We present a new algorithm,prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems.

Expertise consists of rapid selection and application of compiled experience. Robust reasoning, however, requires adaptation to new contingencies and intelligent modification of past experience. This article presents a comprehensive computational model of analogical (case-based) reasoning that transitions smoothly between case replay, case adaptation, and general problem solving, exploiting and modifying past experience when available and resorting to general problem-solving methods when required. Learning occurs by accumulation of new cases, especially in situations that required extensive problem solving, and by tuning the indexing structure of the memory model to retrieve progressively more appropriate cases. The derivational replay mechanism is discussed in some detail, and extensive results of the first full implementation are presented.

Many modern grammatical formalisms divide the task of linguistic specification into a context free component of phrasal constraints and a separate component of attribute-value or functional constraints. Conventional methods for recognizing the strings of a language also divide into two parts so that they can exploit the different computational properties of these components. This paper focuses on the interface between these components as a source of computational complexity distinct from the complexity internal to each. We first analyze the common hybrid strategy in which a polynomial context-free parser is modified to interleave functional constraint solving with context-free constituent analysis. This strategy depends on the property of monotonicity in order to prune unnecessary computation.

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems.

FOIL is a learning system that constructs Horn clause programs from examples. This paper summarises the development of FOIL from 1989 up to early 1993 and evaluates its effectiveness on a non-trivial sequence of learning tasks taken from a Prolog programming text. Although many of these tasks are handled reasonably well, the experiment highlights some weaknesses of the current implementation. Areas for further research are identified.

In VISION-21 Symposium. NASA Lewis Research Center and the Ohio Aerospace Institute.

Blackwell.

Problem solving with incomplete information is usually very costly, since multiple alternatives must be taken into account in the planning pro cess. In this paper, we present some pruning rules that lead to substantial cost savings. The rules are all based on the simple idea that, if goal achievement is the sole criterion for performance, a planner need not consider one "branch" in its search space when there is another "branch" characterized by equal or greater information. The idea is worked out for the cases of sequential planning, conditional planning, and interleaved planning and execution. The rules are of special value in this last case, as they provide a way for the problem solver to terminate its search without planning all the way to the goal and yet be assured that no important alternatives are overlooked.

Abduction is inference to the best explanation.