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  1. Intractability and the use of heuristics in psychological explanations.Iris van Rooij, Cory Wright & Todd Wareham - 2012 - Synthese 187 (2):471-487.
    Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.
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  • On the computational complexity of ethics: moral tractability for minds and machines.Jakob Stenseke - 2024 - Artificial Intelligence Review 57 (105):90.
    Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...)
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  • Discovering hidden structure in factored MDPs.Andrey Kolobov, Mausam & Daniel S. Weld - 2012 - Artificial Intelligence 189 (C):19-47.
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  • Kernel functions for case-based planning.Ivan Serina - 2010 - Artificial Intelligence 174 (16-17):1369-1406.
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  • Expressive equivalence of planning formalisms.Christer Bäckström - 1995 - Artificial Intelligence 76 (1-2):17-34.
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  • Towards efficient universal planning: A randomized approach.Peter Jonsson, Patrik Haslum & Christer Bäckström - 2000 - Artificial Intelligence 117 (1):1-29.
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  • The Tractable Cognition Thesis.Iris Van Rooij - 2008 - Cognitive Science 32 (6):939-984.
    The recognition that human minds/brains are finite systems with limited resources for computation has led some researchers to advance the Tractable Cognition thesis: Human cognitive capacities are constrained by computational tractability. This thesis, if true, serves cognitive psychology by constraining the space of computational‐level theories of cognition. To utilize this constraint, a precise and workable definition of “computational tractability” is needed. Following computer science tradition, many cognitive scientists and psychologists define computational tractability as polynomial‐time computability, leading to the P‐Cognition thesis. (...)
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  • A lightweight epistemic logic and its application to planning.Martin C. Cooper, Andreas Herzig, Faustine Maffre, Frédéric Maris, Elise Perrotin & Pierre Régnier - 2021 - Artificial Intelligence 298 (C):103437.
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  • State space search nogood learning: Online refinement of critical-path dead-end detectors in planning.Marcel Steinmetz & Jörg Hoffmann - 2017 - Artificial Intelligence 245 (C):1-37.
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  • Optimal admissible composition of abstraction heuristics.Michael Katz & Carmel Domshlak - 2010 - Artificial Intelligence 174 (12-13):767-798.
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  • Applicability conditions for plans with loops: Computability results and algorithms.Siddharth Srivastava, Neil Immerman & Shlomo Zilberstein - 2012 - Artificial Intelligence 191-192 (C):1-19.
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  • Limitations of acyclic causal graphs for planning.Anders Jonsson, Peter Jonsson & Tomas Lööw - 2014 - Artificial Intelligence 210 (C):36-55.
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  • Maintenance goals of agents in a dynamic environment: Formulation and policy construction.Chitta Baral, Thomas Eiter, Marcus Bjäreland & Mutsumi Nakamura - 2008 - Artificial Intelligence 172 (12-13):1429-1469.
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  • The complexity of achievement and maintenance problems in agent-based systems.Iain A. Stewart - 2003 - Artificial Intelligence 146 (2):175-191.
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  • Fast planning through planning graph analysis.Avrim L. Blum & Merrick L. Furst - 1997 - Artificial Intelligence 90 (1-2):281-300.
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  • Computational complexity of planning and approximate planning in the presence of incompleteness.Chitta Baral, Vladik Kreinovich & Raúl Trejo - 2000 - Artificial Intelligence 122 (1-2):241-267.
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  • Parameterized Complexity of Theory of Mind Reasoning in Dynamic Epistemic Logic.Iris van de Pol, Iris van Rooij & Jakub Szymanik - 2018 - Journal of Logic, Language and Information 27 (3):255-294.
    Theory of mind refers to the human capacity for reasoning about others’ mental states based on observations of their actions and unfolding events. This type of reasoning is notorious in the cognitive science literature for its presumed computational intractability. A possible reason could be that it may involve higher-order thinking. To investigate this we formalize theory of mind reasoning as updating of beliefs about beliefs using dynamic epistemic logic, as this formalism allows to parameterize ‘order of thinking.’ We prove that (...)
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  • A planner agent that tries its best in presence of nondeterminism.Silvio do Lago Pereira & Leliane Nunes de Barros - 2012 - Journal of Applied Logic 10 (4):419-430.
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  • Algorithms and conditional lower bounds for planning problems.Krishnendu Chatterjee, Wolfgang Dvořák, Monika Henzinger & Alexander Svozil - 2021 - Artificial Intelligence 297 (C):103499.
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  • Computing programs for generalized planning using a classical planner.Javier Segovia-Aguas, Sergio Jiménez & Anders Jonsson - 2019 - Artificial Intelligence 272 (C):52-85.
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  • Conformant planning via heuristic forward search: A new approach.Jörg Hoffmann & Ronen I. Brafman - 2006 - Artificial Intelligence 170 (6-7):507-541.
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  • Contingent planning under uncertainty via stochastic satisfiability.Stephen M. Majercik & Michael L. Littman - 2003 - Artificial Intelligence 147 (1-2):119-162.
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  • Planning with mental models – Balancing explanations and explicability.Sarath Sreedharan, Tathagata Chakraborti, Christian Muise & Subbarao Kambhampati - 2024 - Artificial Intelligence 335 (C):104181.
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  • The deterministic part of the seventh International Planning Competition.Carlos Linares López, Sergio Jiménez Celorrio & Ángel García Olaya - 2015 - Artificial Intelligence 223 (C):82-119.
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  • Complexity results for standard benchmark domains in planning.Malte Helmert - 2003 - Artificial Intelligence 143 (2):219-262.
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  • Connectionist semantic systematicity.Stefan L. Frank, Willem F. G. Haselager & Iris van Rooij - 2009 - Cognition 110 (3):358-379.
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  • Decidability and complexity of action-based temporal planning over dense time.Nicola Gigante, Andrea Micheli, Angelo Montanari & Enrico Scala - 2022 - Artificial Intelligence 307 (C):103686.
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  • Concise finite-domain representations for PDDL planning tasks.Malte Helmert - 2009 - Artificial Intelligence 173 (5-6):503-535.
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  • On the computational complexity of temporal projection, planning, and plan validation.Bernhard Nebel & Christer Bäckström - 1994 - Artificial Intelligence 66 (1):125-160.
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  • Heuristics for planning with penalties and rewards formulated in logic and computed through circuits.Blai Bonet & Héctor Geffner - 2008 - Artificial Intelligence 172 (12-13):1579-1604.
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  • Landmark-based approaches for goal recognition as planning.Ramon Fraga Pereira, Nir Oren & Felipe Meneguzzi - 2020 - Artificial Intelligence 279 (C):103217.
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  • Blocks World revisited.John Slaney & Sylvie Thiébaux - 2001 - Artificial Intelligence 125 (1-2):119-153.
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  • The influence of k-dependence on the complexity of planning.Omer Giménez & Anders Jonsson - 2012 - Artificial Intelligence 177-179 (C):25-45.
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  • The MADLA planner: Multi-agent planning by combination of distributed and local heuristic search.Michal Štolba & Antonín Komenda - 2017 - Artificial Intelligence 252 (C):175-210.
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  • Conformant plans and beyond: Principles and complexity.Blai Bonet - 2010 - Artificial Intelligence 174 (3-4):245-269.
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  • On the undecidability of probabilistic planning and related stochastic optimization problems.Omid Madani, Steve Hanks & Anne Condon - 2003 - Artificial Intelligence 147 (1-2):5-34.
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  • Plan reuse versus plan generation: a theoretical and empirical analysis.Bernhard Nebel & Jana Koehler - 1995 - Artificial Intelligence 76 (1-2):427-454.
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  • Backdoors to planning.Martin Kronegger, Sebastian Ordyniak & Andreas Pfandler - 2019 - Artificial Intelligence 269 (C):49-75.
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  • DA2 merging operators.S. Konieczny, J. Lang & P. Marquis - 2004 - Artificial Intelligence 157 (1-2):49-79.
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  • State-variable planning under structural restrictions: algorithms and complexity.Peter Jonsson & Christer Bäckström - 1998 - Artificial Intelligence 100 (1-2):125-176.
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  • From model checking to equilibrium checking: Reactive modules for rational verification.Julian Gutierrez, Paul Harrenstein & Michael Wooldridge - 2017 - Artificial Intelligence 248 (C):123-157.
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  • CPCES: A planning framework to solve conformant planning problems through a counterexample guided refinement.Alban Grastien & Enrico Scala - 2020 - Artificial Intelligence 284 (C):103271.
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  • Star-topology decoupled state space search.Daniel Gnad & Jörg Hoffmann - 2018 - Artificial Intelligence 257 (C):24-60.
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  • Red–black planning: A new systematic approach to partial delete relaxation.Carmel Domshlak, Jörg Hoffmann & Michael Katz - 2015 - Artificial Intelligence 221 (C):73-114.
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  • A probabilistic analysis of prepositional STRIPS planning.Tom Bylander - 1996 - Artificial Intelligence 81 (1-2):241-271.
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  • On the complexity of planning for agent teams and its implications for single agent planning.Ronen I. Brafman & Carmel Domshlak - 2013 - Artificial Intelligence 198 (C):52-71.
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  • Specifying and computing preferred plans.Meghyn Bienvenu, Christian Fritz & Sheila A. McIlraith - 2011 - Artificial Intelligence 175 (7-8):1308-1345.
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  • A framework for analysing state-abstraction methods.Christer Bäckström & Peter Jonsson - 2022 - Artificial Intelligence 302 (C):103608.
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