On the synthesis of a reactive module
POPL '89 Proceedings of the 16th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Model checking
Dynamic Logic
Alternating-time temporal logic
Journal of the ACM (JACM)
Model checking knowledge, strategies, and games in multi-agent systems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Electronic Notes in Theoretical Computer Science (ENTCS)
An algebraic definition of simulation between programs
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Automatic synthesis of new behaviors from a library of available behaviors
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automated composition of web services by planning at the knowledge level
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Synthesis of reactive(1) designs
VMCAI'06 Proceedings of the 7th international conference on Verification, Model Checking, and Abstract Interpretation
Decision theoretic behavior composition
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Qualitative approximate behavior composition
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Automatic behavior composition synthesis
Artificial Intelligence
| Hi-index | 0.00 |
Agent composition is the problem of realizing a "virtual" agent by suitably directing a set of available "concrete", i.e., already implemented, agents. It is a synthesis problem, since its solution amounts to synthesizing a controller that suitably directs the available agents. Agent composition has its roots in certain forms of service composition advocated for SOA, and it has been recently actively studied by AI and Agents community. In this paper, we show that agent composition can be solved by ATL (Alternating-time Temporal Logic) model checking. This results is of interest for at least two contrasting reasons. First, from the point of view of agent composition, it gives access to some of the most modern model checking techniques and state of the art tools, such as MCMAS, that have been recently developed by the Agent community. Second, from the point of view of ATL verification tools, it gives a novel concrete problem to look at, which puts emphasis on actually synthesize winning policies (the controller) instead of just checking that they exist.