Plan recognition is an important task whenever a system has to take into account an agent’s actions and goals in order to be able to react adequately. Most plan recognizers work by merely maintaining a set of equally plausible plan hypotheses each of which proved compatible with recent observations without taking into account individual preferences of the currently observed agent. Such addition...

In this paper we outline a model for plan recognition in advice-giving settings which incorporates user modeling techniques and we show how to extend it to allow a wider range of user feedback than in previous plan recognition models. In particular, we discuss how this model allows for clariication dialogues both in cases where there are faults in a user's plan and in cases where alternate deco...

There is an increasing need to develop artificial intelligence systems that assist groups of humans working on coordinated tasks. These must recognize and understand the plans relationships between actions for a team toward common objective. This article reviews literature plan recognition surveys most recent logic-based approaches implementing it. First, we provide some background knowledge, i...

In this paper, we propose a system that takes the attendance of students for classroom lecture. Our system takes the attendance automatically using face recognition. However, it is difficult to estimate the attendance precisely using each result of face recognition independently because the face detection rate is not sufficiently high. In this paper, we propose a method for estimating the atten...

Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming comp...

Plan libraries are the most important knowledge source of many plan recognition systems. The plan decompositions they contain provide information about how a plan has to be executed to actually achieve its associated goals and be recognized by the system. This paper presents an approach to the automatic acquisition of plan decompositions from sample action sequences. In particular a clustering ...

This article discusses how explanation-based learning of plan schemata from observation can improve performance of plan recognition. The GENESIS program is presented as an implemented system for narrative text understanding that learns schemata and improves its performance. Learned schemata allow GENESIS to use schema-based understanding techniques when interpreting events and thereby ovoid the...

How to characterize a literary genre is a much debated problem, which can be approached with useful results by combining models drawn from both Literary Theory and Computer Science. Once a genre is specified with some rigour in a constructive way, it becomes possible not only to determine whether a given plot is a legitimate representative of the genre, but also to generate such plots, an abili...

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observed activity sequences (team traces) of a set of intelligent agents, based on a library of known team activity sequences (team plans). Previous MAPR systems require that team traces and team plans are fully observed. In this paper we relax this constraint, i.e., team traces and tea...