Recognizing the mental-state, i.e., the beliefs, desires, plans, and intentions, of other agents situated in the environment is an important part of intelligent activity. Doing this with limited resources and in a continuously changing environment, where agents are continuously changing their mind, is a challenging task. In this paper, we provide algorithms for performing reactive plan recognit...

We have developed a multiagent scheme which utilizes plan recognition as its primary means of acquiring the information necessary to coordinate the activities of agents. Pr~llmlnary research has demonstrated that the plan recognition system developed makes coordination of multiple agents possible. An important issue that arises when observation is the primary means of information acquisition is...

In this paper we explore challenges related to the engineering of plan libraries for plan recognition. This is an often overlooked problem, yet essential in the design of any real world plan recognizers. We mainly discuss challenges related to the evaluation of equivalence between plan libraries. We explain why this is a potential source of ill-conceived plan libraries. We consider an existing ...

Typical approaches to plan recognition start from a representation of an agent’s possible plans, and reason evidentially from observations of the agent’s actions to assess the plausibility of the various candidates. A more expansive view of the task (consistent with some prior work) accounts for the context in which the plan was generated, the mental state and planning process of the agent, and...

There are today a lot of different techniques for performing plan recognition in various domains, such as Intelligent Tutoring Systems, Human-Computer Interaction, or multi agent Systems. In order to bring theory into practice, we claim that experiments should be carried out a larger scales than what is done today. To do so, we stress on the importance of building incomplete plan recognition sy...

In classical planning it is easy to verify if a given sequence of actions is a solution to a planning problem. It has to be checked whether the actions are applicable in the given order and if a goal state is reached after executing them. In this paper we show that verifying whether a plan is a solution to an HTN planning problem is much harder. More specifically, we prove that this problem is ...

Goal recognition is the problem of inferring the goal of an agent, based on its observed actions. An inspiring approach—plan recognition by planning (PRP)—uses off-the-shelf planners to dynamically generate plans for given goals, eliminating the need for the traditional plan library. However, existing PRP formulation is inherently inefficient in online recognition, and cannot be used with motio...

We propose a memory-based approach to the problem of goal-schema recognition. We use a generic episodic memory module to perform incremental goal schema recognition and to build the plan library. Unlike other case-based plan recognizers it does not require complete knowledge of the planning domain or the ability to record intermediate planning states. Similarity of plans is computed incremental...