This paper presents preliminary results of our work with a major financial company, where we try to use methods of plan recognition in order to investigate the interactions of a costumer with the company’s online interface. In this paper, we present the first steps of integrating a plan recognition algorithm in a real-world application for detecting and analyzing the interactions of a costumer....

Expert systems are typically expected to be able to justify their decisions to the user. This paper argues that help systems or tutoring systems based on a plan recognizer can equally benefit from an explanation component. To this end a plan recognition system equippedwith a usermodel is presented and the techniques required to generate precise and useful justifications of its results are intro...

This paper presents a plan recognition algorithm for inferring student behavior using virtual science laboratories. The algorithm extends existing plan recognition technology and was integrated with an existing educational application for chemistry. Automatic recognition of students’ activities in virtual laboratories can provide important information to teachers as well as serve as the basis f...

This paper describes a challenging plan recognition problem that arises in environments in which agents engage widely in exploratory behavior, and presents new algorithms for effective plan recognition in such settings. In exploratory domains, agents’ actions map onto logs of behavior that include switching between activities, extraneous actions, and mistakes. Flexible pedagogical software, suc...

This paper draws its inspiration from current work in reactive planning to guide plan recognition using \plans as recipes". The plan recognition process guided by such a library of plans is called means-end plan recognition. An extension of dynamic logic, called dynamic agent logic, is introduced to provide a formal semantics for means-end plan recognition and its counterpart, means-end plan ex...

Goal Recognition is the task of discerning intended goal an agent given a sequence observations, whereas Plan consists identifying plan to achieve such goal. Regardless underlying techniques, most recognition approaches are directly affected by quality available observations. In this paper, we develop neuro-symbolic that can combine learning and planning compensating for noise missing observati...

Plan recognition, activity and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment agents. This sy

Traditional plan recognition systems try to recognize a user's domain plan from the user's queries. In contrast, the Interactive Discourse Planner (IDP) tries to recognize an expansion of its own text plan from the user's queries. During interaction, IDP builds a text plan in reaction to user feedback that it uses as the discourse context. IDP uses this text plan to analyze user questions for i...

The ability of recognizing a multi-agent plan a plan which has been generated for multiple executing agents implies the skill of recognizing the plan underlying a collective activity in which a group of agents is involved. Multi-agent plan recognition is a necessary and useful extension of current PR systems in some domains. In this work starting from CHAPLIN (CHart Applied to PLan INference, a...