We address the problem of designing programming systems to reason with and about constraints. Taking a logic programming approach, we define a class of programming languages, the CLP languages, all of which share the same essential semantic properties. From a conceptual point of view, CLP programs are highly declarative and are soundly based within a unified framework of formal semantics. This framework not only subsumes that of logic programming, but satisfies the core properties of logic programs more naturally. From a user's point of view, CLP programs have great expressive power due to the constraints which they naturally manipulate.

In AAAI-87.

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In this paper, the reasoning and planning capabilities of an autonomous mobile robot are described; The reasoning system that controls the robot is designed to exhibit the kind of behavior expected of a rational agent, and is endowed with the psychological attitudes of belief, desire, and intention. Because these attitudes are explicitly represented, they can be manipulated and reasoned about, resulting in complex goal-directed and reflective behaviors. Unlike most planning systems, the plans or intentions formed by the robot need only be partly elaborated before it decides to act. This allows the robot to avoid overly strong expectations about the environment, overly constrained plans of action, and other forms of overcommitment common to previous planners. In addition, the robot is continuously reactive and has the ability to change its goals and intentions as situations warrant. The system has been tested with SRI's autonomous robot (Flakey) in a space station scenario involving navigation and the performance of emergency tasks. 1

In Automatic Control—World Congress, 1987: Selected Papers from the 10th Triennial World Congress of the International Federation of Automatic Control, pp. 221–226.

Computational Intelligence, 3 (2), 78-93.

AI Magazine, 3

This paper describes a domain-independent implementation of explanation-based generalization (EBG) within a logic-programming environment. Explanation is interleaved with generalization, so that as the training instance is proven to be a positive example of the goal concept, the generalization is simultaneously created. All aspects of the EBG task are viewed in logic, which provides a clear semantics for EBG, and allows its integration into the logic-programming system. In this light operationally becomes a property requiring explicit reasoning. Additionally, viewing EBG in logic clarifies the relation of learning search-control to EBG, and suggests solutions for dealing with imperfect domain theories.

The system presented here shows how Soar, an architecture for general problem solving and learning, can acquire general search-control knowledge from outside guidance. The guidance can be either direct advice about what the system should do, or a problem that illustrates a relevant idea. The system makes use of the guidance by first formulating an appropriate goal for itself. In the process of achieving this goal, it learns general search-control chunks. In the case of learning from direct advice, the goal is to verify that the advice is correct. The verification allows the system to obtain general conditions of applicability of the advice, and to protect itself from erroneous advice. The system learns from illustrative problems by setting the goal of solving the problem provided. It can then transfer the lessons it learns along the way to its original problem. This transfer constitutes a rudimentary form of analogy.

In IJCAI-87, Proc. Tenth Intl. Joint Conference on Artificial Intelligence, Milan, Italy: Morgan Kaufmann, 1987, Vol. 1, pp. 264-270.