Hierarchical Goal Recognition

Blaylock, Nate; Allen, James F.

doi:10.1016/b978-0-12-398532-3.00001-4

Cited by 23 publications

(23 citation statements)

References 12 publications

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“…More recent work has continued developing methods for greater expressiveness without sacrificing tractability. For example, Blaylock and Allen [6] provided a novel HMM-based model that allows efficient exact reasoning about hierarchical goals. Hu and Yang [27] model interacting and concurrent goals.…”

Section: Background: Adversarial Plan Recognitionmentioning

confidence: 99%

Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

Avrahami-Zilberbrand

Kaminka

2014

Plan, Activity, and Intent Recognition

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Section: Background: Adversarial Plan Recognitionmentioning

confidence: 99%

Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

Avrahami-Zilberbrand

Kaminka

2014

Plan, Activity, and Intent Recognition

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“…Table shows the performance in the Monroe domain. This domain is an easier domain than the Linux one, and the literature or plan recognition reports prediction accuracies between 95% to 99% for different methods (e.g., Blaylock and Allen report a precision of 95.6% in this dataset). In this case, we observe that our refinement‐based similarity outperforms all other similarity measures, reaching perfect classification when using 3‐NN and 5‐NN.…”

Section: Methodsmentioning

confidence: 99%

Structural plan similarity based on refinements in the space of partial plans

López

Ontañón

2017

Computational Intelligence

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Plan similarity measures play a key role in many areas of artificial intelligence, such as case‐based planning, plan recognition, ambient intelligence, or digital storytelling. In this paper, we present 2 novel structural similarity measures to compare plans based on a search process in the space of partial plans. Partial plans are compact representations of sets of plans with some common structure and can be organized in a lattice so that the most general partial plans are above the most specific ones. To compute our similarity measures, we traverse this space of partial plans from the most general to the most specific using successive refinements. Our first similarity measure is designed for propositional plan formalisms, and the second is designed for classical planning formalisms (including variables and types). We also introduce 2 novel refinement operators used to traverse the space of plans: an ideal downward refinement operator for propositional partial plans and a finite and complete downward refinement operator for classical partial plans. Finally, we evaluate our similarity measures in the context of a nearest neighbor classifier using 2 datasets commonly used in the plan recognition literature (Linux and Monroe), showing good results in both synthetic and real data.

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“…discrete full (Geib and Goldman, 2009) yes no - * yes no - * * discrete partial (Ramírez and Geffner, 2009) no no yes yes no - * * discrete full (Baker et al, 2009) yes yes yes yes no - * * discrete full (Avrahami-Zilberbrand and Kaminka, 2007) yes no yes yes no - * * mixed/disc. partial (Blaylock and Allen, 2006) yes no - * yes no - * * discrete full (Avrahami-Zilberbrand and no no yes yes no - * * mixed/disc. full no no yes yes no - * * mixed/disc.…”

Section: Plan Recognition By Planning In Domain Modelsmentioning

confidence: 99%

“…Closely related is the work of Allen (2004, 2003), who compute goal probabilities as a product of conditional action probabilities which are learned using a corpus of observed plan executions. This work was later extended to recognise hierarchical sub-goals (Blaylock and Allen, 2006).…”

Section: Plan Recognition By Similarity To Past Plansmentioning

confidence: 99%

Autonomous agents modelling other agents: A comprehensive survey and open problems

Albrecht

Stone

2018

Artificial Intelligence

300

208

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Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.

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Hierarchical Goal Recognition

Cited by 23 publications

References 12 publications

Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

Structural plan similarity based on refinements in the space of partial plans

Autonomous agents modelling other agents: A comprehensive survey and open problems

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