Nonmonotonic logic and temporal projection
Artificial Intelligence
Specification matching of software components
ACM Transactions on Software Engineering and Methodology (TOSEM)
Proving concurrent constraint programs correct
ACM Transactions on Programming Languages and Systems (TOPLAS)
Theory for coordinating concurrent hierarchical planning agents using summary information
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Dynamic Logic
On illegal composition of first-class agent interaction protocols
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
Engineering Societies in the Agents World VIII
Specialization of Interaction Protocols in a Temporal Action Logic
Electronic Notes in Theoretical Computer Science (ENTCS)
Using constraints and process algebra for specification of first-class agent interaction protocols
ESAW'06 Proceedings of the 7th international conference on Engineering societies in the agents world VII
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Many practitioners view agent interaction protocols as rigid specifications that are defined a priori , and hard-code their agents with a set of protocols known at design time -- an unnecessary restriction for intelligent and adaptive agents. To achieve the full potential of multi-agent systems, we believe that it is important that multi-agent interaction protocols are treated as first-class computational entities in systems. That is, they exist at runtime in systems as entities that can be referenced, inspected, composed, invoked and shared, rather than as abstractions that emerge from the behaviour of the participants. Using first-class protocols, a goal-directed agent can assess a library of protocols at runtime to determine which protocols best achieve a particular goal. In this paper, we presented three methods that enable agents to determine if a protocol achieves a specified goal. The two most promising approaches allow an agent to summarise a protocol that it has learned by calculating the outcomes achieved by the protocol, and annotate the protocol with these summaries. The agent can match , via annotations, which protocols in a library achieve a given goal.