Browsing by Author "Fadnis, Saurabh"

Now showing 1 - 3 of 3
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    Analytics of Condition-Effect Rules
    (2019-03-11) Fadnis, Saurabh; Rintanen, Jussi; Sähkötekniikan korkeakoulu; Janhunen, Tomi
    This thesis studies properties such as confluence and termination for a rule model with condition-effect rules. A rule model is first defined and the complexity of solving these problems is analysed. Analysis of both confluence and termination shows that they are PSPACE-complete for our rule model. We give algorithms for testing these properties. We also study certain syntactic and structural restrictions under which these problems become easier and can be solved in polynomial time for practical purposes.
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    Generalized 3-Valued Belief States in Conformant Planning
    (Springer, 2022-11) Rintanen, Jussi; Fadnis, Saurabh; Department of Computer Science; Khanna, Sankalp; Cao, Jian; Bai, Quan; Xu, Guandong; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML); Professorship Rintanen Jussi; Professorship Rintanen Jussi
    The high complexity of planning with partial observability has motivated to find compact representations of belief state (sets of states) that reduce their size exponentially, including the 3-valued literal-based approximations by Baral et al. and tag-based approximations by Palacios and Geffner. We present a generalization of 3-valued literal-based approximations, and an algorithm that analyzes a succinctly represented planning problem to derive a set of formulas the truth of which accurately represents any reachable belief state. This set is not limited to literals and can contain arbitrary formulas. We demonstrate that a factored representation of belief states based on this analysis enables fully automated reduction of conformant planning problems to classical planning, bypassing some of the limitations of earlier approaches.
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    Planning with Partial Observability by SAT
    (Springer, 2023) Fadnis, Saurabh; Rintanen, Jussi; Department of Computer Science; Gaggl, Sarah; Martinez, Maria Vanina; Ortiz, Magdalena; Professorship Rintanen Jussi; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML); Professorship Rintanen Jussi
    Geffner & Geffner (2018) have shown that finding plans by reduction to SAT is not limited to classical planning, but is competitive also for fully observable non-deterministic planning. This work extends these ideas to planning with partial observability. Specifically, we handle partial observability by requiring that during the execution of a plan, the same actions have to be taken in all indistinguishable circumstances. We demonstrate that encoding this condition directly leads to far better scalability than an explicit encoding of observations-to-actions mapping, for high numbers of observations.