Plan recognition has been widely used in agents that need to infer which plans are being executed or which activities are being performed by others. In many applications, reasoning and acting in response to plan recognition requires time. In such systems, plan recognition is expected to be made not only with precision, but also in a timely fashion. When recognition cannot be made in time, an ag...

The ability to recognize the plans and goals of other agents enables humans to reason about what other people are doing, why they are doing it, and what they will do next. This fundamental cognitive capability is also critical to interpersonal interactions because human communications presuppose an ability to understand the motivations of the participants and subjects of the discussion. As the ...

Intelligent agents who are situated in nuiltiagent domains must reason about one anotIters' actions and plans. Following the tradition of earlier work in A l , we present a model of plan recognition as belief and intention ascript ion, an inherently defeasible reasoning process. However, we encode this process using a direct argumentation system. Wi th in this system, we can make explicit state...

Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent’s task given a sufficiently long action sequence. However, by the time the plan and/or goal are recognized, it ...

Problem-Solving: Schmidt linearly ordered and unbounded in time. Plans are bounded, hierarchical, and nonlinearly ordered based on the logical connection between an enduring outcome of one action and the precondition of some subsequent action. Further, a plan is well-formed with reference to the planner's beliefs about the world rather than with respect to the actual state of the world. For the...

We propose a formal procedure-based agent model for plan recognition as an alternative to the declarative proposition-based agent model. Our formalism, called the Procedural Agent Model (PAM) allows us to describe procedural plans of agents in a concise and abstract way. Plan recognition uses PAM as a library of plan schemas and applies a graph-based pattern-matching algorithm to the observed e...

We present a method for context-dependent and incremental intention recognition by means of incrementally constructing a Bayesian Network (BN) model as more actions are observed. It is achieved with the support of a knowledge base of readily maintained and constructed fragments of BNs. The simple structure of the fragments enables to easily and efficiently acquire the knowledge base, either fro...

Introduction This paper describes ongoing efforts to extend our work (Geib & Goldman 2001b; 2001a) (G&G) on the Probabilistic Hostile Agent Task Tracker (PHATT) to handle the problem of goal abandonment. As such, we will be discussing both probabilistic intent inference and the PHATT system and assume the reader is familiar with these areas. We refer the interested reader to the complete paper ...