A Plan Recognition Algorithm Based on the Probabilistic Goal Graph

2011 ◽  
Author(s):  
Liu Ying ◽  
Gu Wen-xiang
Keyword(s):  
Recognition Algorithm ◽  
Plan Recognition ◽  
Goal Graph

scholarly journals Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search

AI Magazine ◽  
2015 ◽  
Vol 36 (2) ◽  
pp. 10-21 ◽  
Author(s):  
Oriel Uzan ◽  
Reuth Dekel ◽  
Or Seri ◽  
Ya’akov (Kobi) Gal
Keyword(s):  
Learning Environments ◽  
Educational Software ◽  
State Of The Art ◽  
Recognition Algorithm ◽  
Plan Recognition ◽  
Exploratory Learning ◽  
Domain Expert ◽  
Teaching Chemistry ◽  
Exploratory Learning Environments ◽  
New Algorithms

This article presents new algorithms for inferring users’ activities in a class of flexible and open-ended educational software called exploratory learning environments (ELE). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students’ plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.

scholarly journals A probabilistic plan recognition algorithm based on plan tree grammars

2009 ◽  
Vol 173 (11) ◽  
pp. 1101-1132 ◽  
Author(s):  
Christopher W. Geib ◽  
Robert P. Goldman
Keyword(s):  
Recognition Algorithm ◽  
Plan Recognition

scholarly journals Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach

2002 ◽  
Vol 17 ◽  
pp. 83-135 ◽  
Author(s):  
G. A. Kaminka ◽  
D. V. Pynadath ◽  
M. Tambe
Keyword(s):  
Monitoring System ◽  
Scale Up ◽  
Empirical Evaluation ◽  
Recognition Algorithm ◽  
Plan Recognition ◽  
Task Execution ◽  
Team Members ◽  
Human Expert ◽  
Dynamic Task ◽  
Cooperating Agents

Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' *routine* communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.

scholarly journals Heuristic Online Goal Recognition in Continuous Domains

2017 ◽  
Author(s):  
Mor Vered ◽  
Gal A. Kaminka
Keyword(s):  
Recognition Algorithm ◽  
Plan Recognition ◽  
Agent Based ◽  
Online Recognition ◽  
Decision Points ◽  
Team Task ◽  
Goal Recognition ◽  
Continuous Domains ◽  
Run Time

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 motion planners for continuous spaces. In this paper, we utilize a different PRP formulation which allows for online goal recognition, and for application in continuous spaces. We present an online recognition algorithm, where two heuristic decision points may be used to improve run-time significantly over existing work. We specify heuristics for continuous domains, prove guarantees on their use, and empirically evaluate the algorithm over hundreds of experiments in both a 3D navigational environment and a cooperative robotic team task.

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