Stochastic Multi-Agent Plan Recognition, Knowledge Representation and Simulations for Efficient Decision Making

Abstract: Advances in information technology produce large sets of data for decision makers. In both military and civilian efforts to achieve decision superiority, decision makers have to act agilely with proper, adequate and relevant information available. Information fusion is a process aimed to support decision makers’ situation awareness. This involves a process of combining data and information from disparate sources with prior information or knowledge to obtain an improved state estimate about an agent or other relevant phenomena.The important issue in decision making is not only assessing the current situation but also envisioning how a situation may evolve. In this work we focus on the prediction part of decision making called predictive situation awareness. We introduce new methodology where simulations and plan recognition are tools for achieving improved predictive situation awareness.Plan recognition is the term given to the process of inferring an agent’s intentions from a set of actions and is intended to support decision making. Beside its main task that is to support decision makers’ predictive situation awareness, plan recognition could also be used for coordination of actions and for developing computer-game agents that possess cognitive ability to recognize other agents’ behaviour.Successful plan recognition is heavily dependent on the data that is supplied. Therefore we introduce a bridge between plan recognition and sensor management where results of our plan recognition are reused to the control of, to give focus of attention to, the sensors that are expected to acquire the most important/relevant information. Our methodologies include knowledge representation, embedded stochastic simulations, microeconomics, imprecise knowledge and statistical inference issues.