Model checking probabilistic social commitments for intelligent agent communication

Interaction among autonomous agents in Multi-Agent Systems (MASs) is a key aspect for agents to coordinate with one another. Social approaches, as opposed to the mental approaches, have recently received a considerable attention in the area of agent communication. They exploit observable social commitments to develop a verifiable formal semantics through which communication protocols can be specified. Developing and implementing algorithmic model checking for social commitments have been recently addressed. However, model checking social commitments in the presence of uncertainty is yet to be investigated. In this paper, we propose a model checking technique for verifying social commitments in uncertain settings. Social commitments are specified in a modal logical language called Probabilistic Computation Tree Logic of Commitments (PCTLC). The modal logic PCTLC extends PCTL, the probabilistic extension of CTL, with modalities for commitments and their fulfillments. The proposed verification method is a reduction-based model checking technique to the model checking of PCTL. The technique is based upon a set of reduction rules that translate PCTLC formulae to PCTL formulae to take benefit of existing model checkers such as PRISM. Proofs that confirm the soundness of the reduction technique are presented. We also present rules that transform our new version of interpreted systems into models of Markov Decision Processes (MDPs) to be suitable for the PRISM tool. We implemented our approach on top of the PRISM model checker and verified some given properties for the Oblivious Transfer Protocol from the cryptography domain. Our simulation demonstrates the effectiveness of our approach in verifying and model checking social commitments in the presence of uncertainty. We believe that the proposed formal verification technique will advance the literature of social commitments in such a way that not only representing social commitments in uncertain settings is doable, but also verifying them in such settings becomes achievable.