A Survey of Program Slicing Techniques.
A Survey of Program Slicing Techniques.
Efficient algorithms for graph manipulation
Efficient algorithms for graph manipulation
State-Space Reduction Techniques in Agent Verification
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Fighting State Space Explosion: Review and Evaluation
Formal Methods for Industrial Critical Systems
Automated Verification of Multi-Agent Programs
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Property-based Slicing for Agent Verification
Journal of Logic and Computation
A common semantic basis for BDI languages
ProMAS'07 Proceedings of the 5th international conference on Programming multi-agent systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Model checking agent programs by using the program interpreter
CLIMA'10 Proceedings of the 11th international conference on Computational logic in multi-agent systems
MCMAS: a model checker for multi-agent systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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State space reduction techniques have been developed to increase the efficiency of model checking in the context of imperative programming languages. Unfortunately, these techniques cannot straightforwardly be applied to agents: the nature of states in the two programming paradigms differs too much for this to be possible. To resolve this, we adapt core definitions on which existing reduction algorithms are based to agents. Moreover, the framework that we introduce is such that different reduction algorithms can be defined in terms of the same relations. This is beneficial because it enables the reuse of code and reduces computation time when different techniques are used simultaneously. Specifically, we adapt and combine two known techniques: property-based slicing and partial order reduction. We exemplify our work with the Goal agent programming language, and implement the theory that we present for Goal. Several experiments with this implementation show that performance is in line with known results from traditional model checking.