Analyzing generalized planning under nondeterminism

Belle, Vaishak

doi:10.1016/j.artint.2022.103696

Cited by 3 publications

(3 citation statements)

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“…These foundational formulations of generalized planning have been extended in several directions. Bonet et al (2019) develop connections between generalized planning and LTL synthesis; Belle (2022) analyzes the relationships between various correctness criteria in stochastic and non-deterministic settings.…”

Section: Related Workmentioning

confidence: 99%

“…The planning domains used in this reduction include actions that construct components of hierarchical finite-state controllers as well as validate the resulting controllers on the input problem set. This paradigm of evaluation using a finite validation set has been developed along multiple directions to utilize finite sets of positive examples as well as negative examples indicating undesired outcomes of plan execution (Segovia Aguas et al, 2020) and with finite validation sets for use in a general heuristic search process for computing generalized plans (Segovia Aguas et al, 2021, 2022.…”

Section: Meta-level Planning For Computing Generalized Plansmentioning

confidence: 99%

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Hierarchical Decompositions and Termination Analysis for Generalized Planning

Srivastava

2023

jair

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This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of generalized plans has been a longstanding goal in AI, it remains challenging due to fundamental gaps in methods for analyzing the scope and utility of a given generalized plan. This paper addresses these gaps by developing a new conceptual framework along with proof techniques and algorithmic processes for assessing termination and goal-reachability related properties of generalized plans. We build upon classic results from graph theory to decompose generalized plans into smaller components that are then used to derive hierarchical termination arguments. These methods can be used to determine the utility of a given generalized plan, as well as to guide the synthesis and learning processes for generalized plans. We present theoretical as well as empirical results illustrating the scope of this new approach. Our analysis shows that this approach significantly extends the class of generalized plans that can be assessed automatically, thereby reducing barriers in the synthesis and learning of reliable generalized plans.

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Section: Related Workmentioning

confidence: 99%

Section: Meta-level Planning For Computing Generalized Plansmentioning

confidence: 99%

Hierarchical Decompositions and Termination Analysis for Generalized Planning

Srivastava

2023

jair

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“…There are, of course, many works explicating the links between the situation calculus and logic programming; see, for example, Lee and Palla (2012). See also works that link the situation calculus to planning, such as ; Belle (2022); Sardina et al (2004); Baier et al (2007).…”

Section: A Logic For Knowledge and Actionmentioning

confidence: 99%

Toward A Logical Theory Of Fairness and Bias

Belle¹

2023

Theory and Practice of Logic Programming

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Fairness in machine learning is of considerable interest in recent years owing to the propensity of algorithms trained on historical data to amplify and perpetuate historical biases. In this paper, we argue for a formal reconstruction of fairness definitions, not so much to replace existing definitions but to ground their application in an epistemic setting and allow for rich environmental modeling. Consequently we look into three notions: fairness through unawareness, demographic parity and counterfactual fairness, and formalize these in the epistemic situation calculus.

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