Plan Recognition



Parallelizing Plan Recognition

AI Magazine

Modern multicore computers provide an opportunity to parallelize plan recognition algorithms to decrease runtime. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009; Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins.


Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search

AI Magazine

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. 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.


Stanford Heuristic Programming Project

Classics (Collection 2)

For example, in their work on the:Itiggy system, Burton and Brown developed a methodology for automatically generating diagnostic tests that discover the underlying problems responsible for a student's errors However, in subject areas where the student chooses his own problem or when there arc several ways of solving a problem, it is helpful to consider not only the student's final answer but also his steps in producing it and his rationale for taking those steps. Plan recognition is a way of using information about the student's actions in dealing with the combinatorics in domains Miere the number of reasonable solutions and bugs is too large In addition to helping pinpoint the student's misconception, studying his plan is advantageous in that it enables the tutor to offer remediation in the context of the student's problem and his approach to solving it. Figure 2 - An example of NIACSINIA Consultation This paper discusses some approaches to automating the process of plan recognition and using plans to detect misconceptions and provide remediation in context. "dependency graph" relating a student's actions to his beliefs about the problem area no the actions involved via the problem solving methods he used in piecing together.his The recognition of plans in this representation is discussed in section 3, and the confirmation of tentative plans is discussed in section 4.


Stanford Heuristic Programming Project

Classics (Collection 2)

For example, in their work on the:Itiggy system, Burton and Brown developed a methodology for automatically generating diagnostic tests that discover the underlying problems responsible for a student's errors However, in subject areas where the student chooses his own problem or when there arc several ways of solving a problem, it is helpful to consider not only the student's final answer but also his steps in producing it and his rationale for taking those steps. Plan recognition is a way of using information about the student's actions in dealing with the combinatorics in domains Miere the number of reasonable solutions and bugs is too large In addition to helping pinpoint the student's misconception, studying his plan is advantageous in that it enables the tutor to offer remediation in the context of the student's problem and his approach to solving it. Figure 2 - An example of NIACSINIA Consultation This paper discusses some approaches to automating the process of plan recognition and using plans to detect misconceptions and provide remediation in context. "dependency graph" relating a student's actions to his beliefs about the problem area no the actions involved via the problem solving methods he used in piecing together.his The recognition of plans in this representation is discussed in section 3, and the confirmation of tentative plans is discussed in section 4.


Identifying Terrorist Activity with AI Plan Recognition Technology

AI Magazine

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application.


A Bayesian model of plan recognition

Classics

We argue that the problem of plan recognition, inferring an agent's plan from observations, is largely a problem of inference under conditions of uncertainty. We present an approach to the plan recognition problem that is based on Bayesian probability theory. In attempting to solve a plan recognition problem we first retrieve candidate explanations. These explanations (sometimes only the most promising ones) are assembled into a plan recognition Bayesian network, which is a representation of a probability distribution over the set of possible explanations.


Classifying and Detecting Plan-Based Misconceptions for Robust Plan Recognition

AI Magazine

My Ph.D. dissertation (Calistri 1990) extends traditional methods of plan recognition to handle situations in which agents have flawed plans. This extension involves solving two problems: determining what sorts of mistakes people make when they reason about plans and figuring out how to recognize these mistakes when they occur. I have developed a complete classification of plan-based misconceptions, which categorizes all ways that a plan can fail, and I have developed a probabilistic interpretation of these misconceptions that can be used in principle to guide a best-first search algorithm. I have also developed a program called Pathfinder that embodies a practical implementation of this theory.