Memory-efficient algorithms for the verification of temporal properties
Formal Methods in System Design - Special issue on computer-aided verification: general methods
On social laws for artificial agent societies: off-line design
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Formal methods: state of the art and future directions
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
Well-Behaved Borgs, Bolos, and Berserkers
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Adaptive Supervisory Control of Multi-agent Systems
FAABS '00 Proceedings of the First International Workshop on Formal Approaches to Agent-Based Systems-Revised Papers
Efficent Local Model-Checking for Fragments of teh Modal µ-Calculus
TACAs '96 Proceedings of the Second International Workshop on Tools and Algorithms for Construction and Analysis of Systems
Incremental Model Checking in the Modal Mu-Calculus
CAV '94 Proceedings of the 6th International Conference on Computer Aided Verification
On Explicit Plan Languages for Coordinating Multiagent Plan Execution
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Using Artificial Physics to Control Agents
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Journal of Artificial Intelligence Research
Liveness and fairness properties in multi-agent systems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Panel Discussion: Future Directions
FAABS '00 Proceedings of the First International Workshop on Formal Approaches to Agent-Based Systems-Revised Papers
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The increasedprev alence of agents raises numerous practical considerations. This paper addresses three of these - adaptability to unforeseen conditions, behavioral assurance, and timeliness of agent responses. Although these requirements appear contradictory, this paper introduces a paradigm in which all three are simultaneously satisfied. Agent strategies are initially verified. Then they are adapted by learning andformally reverifiedfor behavioral assurance. This paper focuses on improving the time efficiency of reverification after learning. A priori proofs are presentedthat certain learning operators are guaranteedto preserve important classes of properties. In this case, efficiency is maximal because no reverification is needed. For those learning operators with negative a priori results, we present incremental algorithms that can substantially improve the efficiency of reverification.