We present a logical approach to plan recognition that builds on Kautz’s theory of keyhole plan recognition, defined as the problem of inferring descriptions of high-level plans from a set of descriptions of observed, typically low-level, actions. Kautz’s approach is based on a formalization of the structural information contained in an abstraction/decomposition hierarchy of plan schemas: the i...

Goal recognition is generally considered to follow plan recognition. The plan recognition problem is typically defined to be that of identifying which plan in a given library of plans is being executed, given a sequence of observed actions. Once a plan has been identified, the goal of the plan can be assumed to follow. In this work, we address the problem of goal recognition directly, without a...

Most of the available plan recognition techniques are based on the use of a plan library in order to infer user’s intentions and/or strategies. Until some years ago, plan libraries were completely hand coded by human experts, which is an expensive, error prone and slow process. Besides, plan recognition systems with hand-coded plan libraries are not easily portable to new domains, and the creat...

This document describes the results of our work during the first two years of our PhD. studies. The aim of our PhD. thesis is the development of a methodology for automated intrusion detection based on attack plan recognition, and therefore, the design of a general framework for the characterization and theoretical investigation of the plan recognition problem in adversarial scenarios. In the A...

It is important for agents to model other agents’ unobserved plans and goals, based on their observable actions. This process of modeling others based on observations is known as plan-recognition. Plan recognition has been studied for many years. It often takes the form of matching observations of an agent’s actions to a plan-library, a model of possible plans selected by the agent. However, th...

Activity and plan recognition are well-developed research fields, where much work has been done. There has been, however, almost no effort in combining both to create intelligent software that can effectively support the decision-making process faced by human teams enacting a joint plan. In this paper, we present an architecture to integrate activity recognition and plan recognition using a tea...

We present SET-PR, a novel case-based plan recognition algorithm that is tolerant to missing and misclassified actions in its input action sequences. SET-PR uses a novel representation called action sequence graphs to represent stored plans in its plan library and a similarity metric that uses a combination of graph degree sequences and object similarity to retrieve relevant plans from its libr...

Plan recognition techniques frequently make rigid assumptions about the student's plans, and invest substantial effort to infer unobservable properties of the student. The pedagogical benefits of plan recognition analysis are not always obvious. We claim that these difficulties can be overcome if greater attention is paid to the situational context of the student's activity and the pedagogical ...

Plan recognition techniques frequently make rigid assumptions about the student's plans, and invest substantial effort to infer unobservable properties of the student. The pedagogical benefits of plan recognition analysis are not always obvious. We claim that these difficulties can be overcome if greater attention is paid to the situational context of the student's activity and the pedagogical ...