We present a new approach to deductive design synthesis, the Residue Approach, in which designs are represented as sets of constraints. Previous approaches, such as PROLOG [181 or the work of Manna and WaWinger [111, express designs as bindings on single terms. We give a complete and sound procedure for Ending sets of propositions constituting a legal design. The size of the search space of the procedure and the advantages and disadvantages of the Residue Approach are analysed. In particular we show how Residue can avoid backtracking caused by making design decisions of overly coarse granularity. In contrast, it is awkward for the single term approaches to do the same. In addition we give a rule for constraint propagation in deductive synthesis, and show its use in pruning the design space. Finally, Residue is related to other work, in particular, to Default Logic [16] and to Assumption-Based Truth Maintenance [1].

Thi3 framework generalizes previous work on cooperation without communication, and shows the ability of communication to resolve conflicts among agents having disparate goals. Using a deal-making mechanism, agents are able to coordinate and cooperate more easily than in the communication-free model. In addition, there are certain types of interactions where communication makes possible mutually beneficial activity that is otherwise impossible to:oordinate.

Counterfactuals are a form of commonsense non-monotonic inference that has been of long-term interest to philosophers.

Arguments are presented in favor of the answer "yes". The intuitive appeal (or lack thereof) of probabilities is considered briefly. The theoretical adequacies of probabilistic methods are investigated by considering them in light of McCarthy's "typology of uses of non-monotonic reasoning." A quantitative approach which overcomes the usual need for a priori probabilities is presented. Some of the practical advantages of using probabilities in a production system are described.

This article presents an experiment in knowledge-intensive programming within a general problemsolv:ng production-system architecture called Soar In Soar, knowledge is encoded within a set of problem spaces.

From attributes 8 3 Implementation 8 3.1 Overview of Meta-Rulegen 3.2 Algorithm 10 3.2.1 Approach from object rule 11 3.2.2 Approach from attributes 14 4 Preliminary Results 15 5 Conclusion 17 ENHANCING PERFORMANCE OF EXPERT SYSTEMS BY AUTOMATED DISCOVERY OF META-RULES Abstract Machine learning can be used to formulate new meta-level knowledge. A small MYCIN-like medical diagnosis system was constructed as a starting point. Two heuristic methods are used in a program called Meta-Rulegen to form meta-rules from the knowledge base in the diagnosis system. In a preliminary study, 63 meta-rules were formed automatically and, by judiciously selecting a set of meta-rules, the efficiency of the diagnosis system can be improved significantly without degrading the quality of advice. This study suggests that meta-rules can be learned automatically to improve the efficiency of rule-based systems.

Corona is a prototype language for describing designs. The goal is to capture all the information created in the process of designing a component. The information about a design includes the specification of its structure and behavior. It is important to organize this knowledge hierarchically, similar to the way a designer does in refining a design incrementally. Also, we would like to capture the process of the design in addition to its specification.

The single unifying assumption in this work is that one or more of the interacting agents will be using artificial intelligence techniques to guide their actions (including, of course, their communication actions). We call this the "intelligentagent paradigm." Within this broad categorization, the many individual efforts to give Al systems the capability to interact with other rational systems are seen as potentially increasing efficiency (by harnessing multiple reasoners to solve problems in parallel) or as necessitated by the distributed nature of the problem (e.g., distributed air traffic control

Although informal models of evidential reasoning have been successfully app'ied in automated reasoning systems, it is generally difficult to define the range of their applicability In addition, they hay., not provided a basis for coherent management of evidence bearing on hypotheses that are related hierarchically. The Dempster-Shafer (D-S) theory of evidence is appealing because it does suggest a coherent approach for dealing with such relationships However, the theory's complexity and potential for computational inefficiency have tended to discourage its use in reasoning systems In this paper we describe the central elements of the D-S theory, basing our exposition on simple examples drawn from the field of medicine. We then demonstrate the relevance of the 0-S theory to a familiar expert system domain, namely the bacterial organism identification problem that lies at the heart of the MYCIN system. Finally, we present a new adaptation of the D-S approach that achieves computational efficiency while permitting the management of evidential reasoning.within

I. Introduction [he HELIOS system is a tool for conducting research into the design.