Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

Avrahami-Zilberbrand, Dorit; Kaminka, Gal A.

doi:10.1016/b978-0-12-398532-3.00004-x

Cited by 18 publications

(15 citation statements)

References 30 publications

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“…According to the relationships between the observer and the observed agent, the goal or plan recognition could be further divided into keyhole, intended, and adversarial types [ 1 , 75 , 76 , 77 ]. In keyhole plan recognition, the observed agent is indifferent to the fact that its plans are being observed and interpreted.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In keyhole plan recognition, the observed agent is indifferent to the fact that its plans are being observed and interpreted. The presence of a recognizer who is watching the activity of the planning agent does not affect the way he plans and acts [ 59 , 78 ]. Intended recognition arises, for example, in cooperative problem-solving and in understanding indirect speech acts.…”

Section: Background and Related Workmentioning

confidence: 99%

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Single Real Goal, Magnitude-Based Deceptive Path-Planning

Zeng

Qin

et al. 2020

Entropy

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Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent’s final goal before the goal has been reached. It is one of the important approaches to solving real-world challenges, such as public security, strategic transportation, and logistics. Existing methods either cannot make full use of the entire environments’ information, or lack enough flexibility for balancing the path’s deceptivity and available moving resource. In this work, building on recent developments in probabilistic goal recognition, we formalized a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming based deceptive path maximization and generation method. The model helps to establish a computable foundation for any further imposition of different deception concepts or strategies, and broadens its applicability in many scenarios. Experimental results showed the effectiveness of our methods in deceptive path-planning compared to the existing one.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Single Real Goal, Magnitude-Based Deceptive Path-Planning

Zeng

Qin

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…According to the relationships between the observer and the observed agent, the goal or plan recognition could be further divided into keyhole, intended and adversarial types [4,[46][47][48]. In keyhole plan recognition, the observed agent is indifferent to the fact that its plans are being observed and interpreted.…”

Section: Probabilistic Goal Recognitionmentioning

confidence: 99%

Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric

Yin

2019

Entropy

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Recent research has found situations where the identification of agent goals could be purposefully controlled, either by changing the underlying environment to make it easier, or exploiting it during agent planning to delay the opponent’s goal recognition. The paper tries to answer the following questions: what kinds of actions contain less information and more uncertainty about the agent’s real goal, and how to describe this uncertainty; what is the best way to control the process of goal identification. Our contribution is the introduction of a new measure we call relative goal uncertainty (rgu) with which we assess the goal-related information that each action contains. The rgu is a relative value associated with each action and represents the goal uncertainty quantified by information entropy after the action is taken compared to other executable ones in each state. After that, we show how goal vagueness could be controlled either for one side or for both confronting sides, and formulate this goal identification control problem as a mixed-integer programming problem. Empirical evaluation shows the effectiveness of the proposed solution in controlling goal identification process.

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“…The algorithm then (based on feature value) follows the appropriate branch to test in sequence other features until a leaf node is reached. Thus, each leaf node will have pointers to all instance of the plan steps associated with it in the plan library (Avrahami-Zilberbrand, 2009). the plan library with a Feature Decision Tree (FDT) data structure, which efficiently maps observations to matching nodes in the plan library.…”

Section: Feature Decision Tree -Fdtmentioning

confidence: 99%

Predicting Plan Failure by Monitoring Action Sequences and Duration

Farias

Pereira

Hilgert³

et al. 2017

ADCAIJ

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Anticipating failures in agent plan execution is important to enable an agent to develop strategies to avoid or circumvent such failures, allowing the agent to achieve its goal. Plan recognition can be used to infer which plans are being executed from observations of sequences of activities being performed by an agent. In this work, we use this symbolic plan recognition algorithm to find out which plan the agent is performing and develop a failure prediction system, based on plan library information and in simplified calendar that manages the goals the agent has to achieve. This failure predictor is able to monitor the sequence of agent actions and detect if an action is taking too long or does not match the plan that the agent was expected to perform. We showcase this approach successfully in a health-care prototype system.

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Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

Cited by 18 publications

References 30 publications

Single Real Goal, Magnitude-Based Deceptive Path-Planning

Single Real Goal, Magnitude-Based Deceptive Path-Planning

Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric

Predicting Plan Failure by Monitoring Action Sequences and Duration

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