We have de,Jeloped an automated system for the design of laboratory experiments in molecular biology. The system uses a planning method known as skeletal plan refinement that attempts to emulate the human cognitive task of experiment design. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments.

A new method for automated planning, progressive refinement of skeletal plans, has been developed for the problem of experiment design in the domain of molecular biology. The method resulted from a study of the problem-solving behavior of scientists which showed that design usually consisted of lookup of abstracted plans followe6 by hierarchical plan-step refinement. The skeletal plan method has been implemented through two generations of problem-solving systems: the second generation involving a synthesis with the metaplanning approach of Stefik.

BBI, a blackboard system building architecture, ameliorates the Knowledge acquisition bottleneck with generic knowledge sources that learn control heuristics. Some learning knowledge sources replace the knowledge engineer, interacting directly with domain experts Others operate autonomously The paper presents a trace from the illustrative knowledge source. Understand-Preference, running in PROTEAN, a blackboard system for elucidating protein structure Understand-Preference is triggered when a domain expert overrides one of BBI s scheduling recommendations. It identifies and encodes the heuristic underlying the expert s scheduling decision. The trace illustrates how learning knowledge sources exploit 881's rich representation of domain and control knowledge, actions.

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.