Stefan J. Witwicki

Commitment-based service coordination

We present a methodology for composing large-grained services that exhibit temporal uncertainty and complex task dependencies. Our multi-agent approach incorporates temporal and stochastic planning paradigms and commitment-based negotiation to achieve the coordinated provision of services with stochastic outcomes. This is all captured within a service-choreography protocol, by which agents can request future service provisions and receive probabilistic temporal service promises in return, converging on coordinated decisions about how and when to work together. We hypothesise that our approach can scalably converge on good coordination solutions because it partially decouples the problems of negotiating service interactions and computing service policies. Our empirical evaluation provides initial confirmation of this hypothesis, demonstrating the scalability and effectiveness of our approach in producing coordinated, approximately optimal agent policies in a small fraction of the time that an optimal approach requires.

Commitment-driven distributed joint policy search

Decentralized MDPs provide powerful models of interactions in multiagent environments, but are often very difficult or even computationally infeasible to solve optimally. Here we develop a hierarchical approach to solving a restricted set of decentralized MDPs. By forming commitments with other agents and modeling these concisely in their local MDPs, agents effectively, efficiently, and distributively formulate co-ordinated local policies. We introduce a novel construction that captures commitments as constraints on local policies and show how Linear Programming can be used to achieve local optimality subject to these constraints. In contrast to other commitment enforcement approaches, we show ours to be more robust in capturing the intended commitment semantics while maximizing local utility. We also describe a commitment-space heuristic search algorithm that can be used to approximate optimal joint policies. A preliminary empirical evaluation suggests that our approach yields faster approximate solutions than the conventional encoding of the problem as a multiagent MDP would allow and, when wrapped in an exhaustive commitment-space search, will find the optimal global solution.

Online decision-making for scalable autonomous systems

We present a general formal model called MODIA that can tackle a central challenge for autonomous vehicles (AVs), namely the ability to interact with an unspecified, large number of world entities. In MODIA, a collection of possible decision-problems (DPs), known a priori, are instantiated online and executed as decision-components (DCs), unknown a priori. To combine the individual action recommendations of the DCs into a single action, we propose the lexicographic executor action function (LEAF) mechanism. We analyze the complexity of MODIA and establish LEAFs relation to regret minimization. Finally, we implement MODIA and LEAF using collections of partially observable Markov decision process (POMDP) DPs, and use them for complex AV intersection decision-making. We evaluate the approach in six scenarios within a realistic vehicle simulator and present its use on an AV prototype.