Design of a Solver for Multi-Agent Epistemic Planning

The proliferation of agent-based and IoT technologies has e nabled the development of novel applications involving hundreds of agents. Considering that self-drivi ng cars and other autonomous devices that can control several aspects of our daily life are going to be avai lable en mass in just a few years it will not be long until massive systems of autonomous agents, each act ing upon its own knowledge and beliefs to achieve its own (or group) goals, become available and widel y deployed. To maximize the potentials of such autonomous systems, multi-agent planning and scheduling research [1, 8-10, 24, 28] will need to keep pace. Moreover crea ting a plan for multiple agents to achieve a goal will need to take into consideration agents' knowledge and beliefs, to account for aspects like trust, dishonesty, deception, and incomplete knowledge. The plan ning problem in this new setting is referred to as epistemic planning in the literature; that is epistemic planners are not only in terested in the state of the world but also in the knowledge or beliefs of the agents. Nevertheless, reasoning about knowledge and beliefs is not as direct as reasoning on the "physical" state of the world. That is because expressing, for example, belief relations between a group of agents often implies to consider nested and group beliefs that are not easily extracted from the state descrip tion by a human reader. For this reasons it is necessary to develop a complete and accessible action language to model multi-agent epistemic domains [2] and to advance al so in the study of epistemic solvers [4, 19, 23, 26, 34].