Solving Goal Recognition Design Using ASP

Son, Tran Cao; Sabuncu, Orkunt; Schulz-Hanke, Christian; Schaub, Torsten; Yeoh, William

doi:10.1609/aaai.v30i1.10407

Cited by 13 publications

(12 citation statements)

References 11 publications

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“…However, as we will show in the next section, there are a variety of GRD models that comply with the above requirement, such as models in which sensor placement is applied to improve recognition. In particular, various models suggested previously in the literature (e.g., Keren et al, 2014Keren et al, , 2015Keren et al, , 2016bSon, Sabuncu, Schulz-Hanke, Schaub, & Yeoh, 2016;Mirsky, Stern, Gal, & Kalech, 2018) all support monotonic-nd models for which our suggested pruning can be applied to enhance performance.…”

Section: Independent Persistent Monotonic-nd Grd Modelssupporting

confidence: 55%

“…To examine the effect of a diversion bound on the WCD in GRD settings with bounded-suboptimal agents, we evaluated each setting with a bound of 1-3. In addition to our approaches, we examined the approach suggested by Son et al (2016), where answer set programming is used to find and reduce WCD. This approach, which we denote as ASP, was used only for the fully observable models with optimal agents (the only setting supported by ASP).…”

Section: Setupmentioning

confidence: 99%

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Goal Recognition Design in Deterministic Environments

Keren¹,

Gal²,

Karpas³

2019

jair

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Goal recognition design (GRD) facilitates understanding the goals of acting agents through the analysis and redesign of goal recognition models, thus offering a solution for assessing and minimizing the maximal progress of any agent in the model before goal recognition is guaranteed. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines (1) the extent to which actions performed by an agent within the model reveal the agent’s objective; and (2) how best to modify the model so that the objective of an agent can be detected as early as possible. This approach is relevant to any domain in which rapid goal recognition is essential and the model design can be controlled. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. A GRD problem has two components: the analyzed goal recognition setting, and a design model specifying the possible ways the environment in which agents act can be modified so as to facilitate recognition. This work formulates a general framework for GRD in deterministic and partially observable environments, and offers a toolbox of solutions for evaluating and optimizing model quality for various settings. For the purpose of evaluation we suggest the worst case distinctiveness (WCD) measure, which represents the maximal cost of a path an agent may follow before its goal can be inferred by a goal recognition system. We offer novel compilations to classical planning for calculating WCD in settings where agents are bounded-suboptimal. We then suggest methods for minimizing WCD by searching for an optimal redesign strategy within the space of possible modifications, and using pruning to increase efficiency. We support our approach with an empirical evaluation that measures WCD in a variety of GRD settings and tests the efficiency of our compilation-based methods for computing it. We also examine the effectiveness of reducing WCD via redesign and the performance gain brought about by our proposed pruning strategy.

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Section: Independent Persistent Monotonic-nd Grd Modelssupporting

confidence: 55%

Section: Setupmentioning

confidence: 99%

Goal Recognition Design in Deterministic Environments

Keren¹,

Gal²,

Karpas³

2019

jair

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“…Many existing works consider action removal modifications only, which remove actions from a planning domain (Keren, Gal, and Karpas 2014;Son et al 2016;Ang et al 2017;Wayllace, Hou, and Yeoh 2017;Mirsky et al 2019). Action conditioning modification adds preconditions to actions (Keren, Gal, and Karpas 2018).…”

Section: Planning With Goal Sequencesmentioning

confidence: 99%

Extended Goal Recognition Design with First-Order Computation Tree Logic

2022

AAAI

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Goal recognition design (GRD) is the task of modifying environments for aiding observers to recognize the objectives of agents during online observations. The worst case distinctiveness (WCD), a widely used performance measure in GRD research, can fail to provide useful guidance to the redesign process when some goals are too hard to be distinguished. Moreover, the existing WCD-based approaches do not work when an agent aims for a sequence of goals instead of just one goal. The paper presents a new GRD framework called extended goal recognition design (EGRD) for goal recognition that involves multiple goals. The objective of EGRD is to modify an environment to minimize the worst case distinctiveness of a goal condition that describes how an agent can reach a set of goals. A goal condition can be formally expressed in first-order computation tree logic (FO-CTL) that can be evaluated by model checking. We introduce a novel graphical representation of FO-CTL sentences that is suitable for extended goal recognition. Moreover, we present a search algorithm for EGRD with a novel caching mechanism. Our experimental results show that the caching mechanism can greatly speed up our EGRD search algorithm by reusing the previous evaluation of FO-CTL sentences.

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“…This is not necessarily a problem when searching for a few solutions, e.g., optimal solutions Alviano and Dodaro 2016a) or when incorporating preferences (Brewka 2004;Brewka et al 2015;Alviano, Romero, and Schaub 2018). However, there are many situations where reasoning goes beyond simple search for one answer set, for example, planning when certain routes are gradually forbidden (Son et al 2016), finding diverging solutions (Everardo 2017;Everardo et al 2019), reasoning in probabilistic applications (Lee, Talsania, and Wang 2017), or debugging answer sets (Oetsch, Pührer, and Tompits 2018;Gebser et al 2008). Now, if the user is interested in more than a few solutions to gradually identify specific answer sets, tremendous solution spaces can easily become infeasible to comprehend.…”

Section: Introductionmentioning

confidence: 99%

Rushing and Strolling among Answer Sets – Navigation Made Easy

Fichte

Gaggl

Rusovac

2022

AAAI

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Answer set programming (ASP) is a popular declarative programming paradigm with a wide range of applications in artificial intelligence. Oftentimes, when modeling an AI problem with ASP, and in particular when we are interested beyond simple search for optimal solutions, an actual solution, differences between solutions, or number of solutions of the ASP program matter. For example, when a user aims to identify a specific answer set according to her needs, or requires the total number of diverging solutions to comprehend probabilistic applications such as reasoning in medical domains. Then, there are only certain problem specific and handcrafted encoding techniques available to navigate the solution space of ASP programs, which is oftentimes not enough. In this paper, we propose a formal and general framework for interactive navigation toward desired subsets of answer sets analogous to faceted browsing. Our approach enables the user to explore the solution space by consciously zooming in or out of sub-spaces of solutions at a certain configurable pace. We illustrate that weighted faceted navigation is computationally hard. Finally, we provide an implementation of our approach that demonstrates the feasibility of our framework for incomprehensible solution spaces.

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Solving Goal Recognition Design Using ASP

Cited by 13 publications

References 11 publications

Goal Recognition Design in Deterministic Environments

Goal Recognition Design in Deterministic Environments

Extended Goal Recognition Design with First-Order Computation Tree Logic

Rushing and Strolling among Answer Sets – Navigation Made Easy

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