Practical verification of decision-making in agent-based autonomous systems
By Louise Dennis, Michael Fisher, N. Lincoln, Alexei P. Lisitsa, Sandor M. Veres. 2013
114 citations recorded by the source corpus.
We present a verification methodology for analysing the decision-making component in agent-based hybrid systems. Traditionally hybrid automata have been used to both implement and verify such systems, but hybrid automata based modelling, programming and verification techniques scale poorly as the complexity of discrete decision-making increases making them unattractive in situations where complex logical reasoning is required. In the programming of complex systems it has, therefore, become common to separate out logical decision-making into a separate, discrete, component. However, verification techniques have failed to keep pace with this development. We are exploring agent-based logical components and have developed a model checking technique for such components which can then be composed with a separate analysis of the continuous part of the hybrid system. Among other things this allows program model checkers to be used to verify the actual implementation of the decision-making in hybrid autonomous systems.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
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In Artificial Intelligence
- Stuart J. Russell
- Max Tegmark
- Dan Dewey
- Vaishak Belle
- Albert Bifet
- Stuart Armstrong
- Leandro L. Minku
In Computational Theory and Mathematics
Citations in the corpus
- Research Priorities for Robust and Beneficial Artificial Intelligence (2015, background): “In these cases, it may be practical to verify that the system acts correctly given the knowledge that it has, avoiding the problem of modelling the real environment (Dennis et al. 2013).”