Goal and Plan Recognition Design for Plan Libraries

Mirsky, Reuth; Gal, Ya’akov; Stern, Roni; Kalech, Meir

doi:10.1145/3234464

Cited by 21 publications

(37 citation statements)

References 13 publications

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“…Also, based on new assumption of the availability of prior information about the agent's true goal, they further propose a expected-case distinctiveness (ecd) metric that weighs the possible goals based on their likelihoods. Mirsky et al [55] further study the goal and PRD for plan libraries.…”

Section: Goal Recognition Designmentioning

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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Section: Goal Recognition Designmentioning

confidence: 99%

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

Yin

2019

Entropy

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“…More generally, adversarial plan recognition has been applied to the recognition of strategies and tactics of opponents, in which adversarial reasoning is needed to understand the minds of the opponents. Generic methods for plan recognition are based on plan-library [43], inverse-planning [44], and learning [45].…”

Section: Goal Recognitionmentioning

confidence: 99%

“…Goal recognition design focuses on one controllable environment by modifying the configuration of the domain [43]. As for the observer, redesigning the environment will improve explainability of the actor's behavior.…”

Section: Goal Recognition Designmentioning

confidence: 99%

“…Many metrics have been proposed to redesign the environment, such as worst case distinctiveness (wcd) [3], expected-case distinctiveness (ecd) [47], all-goals wcd (wcd (ag) ) [47], and relative goal uncertainty (rgu) [35]. As for more general plan recognition design problem, the worst-case distinctiveness for plans (wcpd) measures the number of observations needed to unambiguously identify the agent's plan [43]. The wcd and goal recognition design (GRD) problem are defined as follow:…”

Section: Goal Recognition Designmentioning

confidence: 99%

“…These metrics for GRD are mainly formulated to optimize the maximum number of observations, which are required to unambiguously infer the goal or plan of the agent [43]. Such as the wcd of a domain D is an upper bound of the action number that the actor can perform in a stable model, before selecting a distinctive path to reveal the goal [3].…”

Section: Goal Recognition Designmentioning

confidence: 99%

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Goal Recognition Control under Network Interdiction Using a Privacy Information Metric

Luo

Wei

et al. 2019

Symmetry

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Goal recognition (GR) is a method of inferring the goals of other agents, which enables humans or AI agents to proactively make response plans. Goal recognition design (GRD) has been proposed to deliberately redesign the underlying environment to accelerate goal recognition. Along with the GR and GRD problems, in this paper, we start by introducing the goal recognition control (GRC) problem under network interdiction, which focuses on controlling the goal recognition process. When the observer attempts to facilitate the explainability of the actor’s behavior and accelerate goal recognition by reducing the uncertainty, the actor wants to minimize the privacy information leakage by manipulating the asymmetric information and delay the goal recognition process. Then, the GRC under network interdiction is formulated as one static Stackelberg game, where the observer obtains asymmetric information about the actor’s intended goal and proactively interdicts the edges of the network with a bounded resource. The privacy leakage of the actor’s actions about the real goals is quantified by a min-entropy information metric and this privacy information metric is associated with the goal uncertainty. Next in importance, we define the privacy information metric based GRC under network interdiction (InfoGRC) and the information metric based GRC under threshold network interdiction (InfoGRCT). After dual reformulating, the InfoGRC and InfoGRCT as bi-level mixed-integer programming problems, one Benders decomposition-based approach is adopted to optimize the observer’s optimal interdiction resource allocation and the actor’s cost-optimal path-planning. Finally, some experimental evaluations are conducted to demonstrate the effectiveness of the InfoGRC and InfoGRCT models in the task of controlling the goal recognition process.

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