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  1. 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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  • An algorithm for rinding MAPs for belief networks through cost-based abduction.Ashraf M. Abdelbar - 1998 - Artificial Intelligence 104 (1-2):331-338.
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  • Rational analysis, intractability, and the prospects of ‘as if’-explanations.Iris van Rooij, Johan Kwisthout, Todd Wareham & Cory Wright - 2018 - Synthese 195 (2):491-510.
    Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...)
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  • Coherence measures and inference to the best explanation.David H. Glass - 2007 - Synthese 157 (3):275-296.
    This paper considers an application of work on probabilistic measures of coherence to inference to the best explanation. Rather than considering information reported from different sources, as is usually the case when discussing coherence measures, the approach adopted here is to use a coherence measure to rank competing explanations in terms of their coherence with a piece of evidence. By adopting such an approach IBE can be made more precise and so a major objection to this mode of reasoning can (...)
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  • Bayesian Intractability Is Not an Ailment That Approximation Can Cure.Johan Kwisthout, Todd Wareham & Iris van Rooij - 2011 - Cognitive Science 35 (5):779-784.
    Bayesian models are often criticized for postulating computations that are computationally intractable (e.g., NP-hard) and therefore implausibly performed by our resource-bounded minds/brains. Our letter is motivated by the observation that Bayesian modelers have been claiming that they can counter this charge of “intractability” by proposing that Bayesian computations can be tractably approximated. We would like to make the cognitive science community aware of the problematic nature of such claims. We cite mathematical proofs from the computer science literature that show intractable (...)
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  • The complexity of approximating MAPs for belief networks with bounded probabilities.Ashraf M. Abdelbar, Stephen T. Hedetniemi & Sandra M. Hedetniemi - 2000 - Artificial Intelligence 124 (2):283-288.
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  • Approximating MAPs for belief networks is NP-hard and other theorems.Ashraf M. Abdelbar & Sandra M. Hedetniemi - 1998 - Artificial Intelligence 102 (1):21-38.
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  • A probabilistic plan recognition algorithm based on plan tree grammars.Christopher W. Geib & Robert P. Goldman - 2009 - Artificial Intelligence 173 (11):1101-1132.
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  • Complexity results for structure-based causality.Thomas Eiter & Thomas Lukasiewicz - 2002 - Artificial Intelligence 142 (1):53-89.
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  • Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering.Ole J. Mengshoel, David C. Wilkins & Dan Roth - 2006 - Artificial Intelligence 170 (16-17):1137-1174.
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  • Monitoring of perception systems: Deterministic, probabilistic, and learning-based fault detection and identification.Pasquale Antonante, Heath G. Nilsen & Luca Carlone - 2023 - Artificial Intelligence 325 (C):103998.
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  • Most frugal explanations in Bayesian networks.Johan Kwisthout - 2015 - Artificial Intelligence 218 (C):56-73.
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  • Complexity of probabilistic reasoning in directed-path singly-connected Bayes networks.Solomon E. Shimony & Carmel Domshlak - 2003 - Artificial Intelligence 151 (1-2):213-225.
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  • Approximate belief updating in max-2-connected Bayes networks is NP-hard.Erez Karpas, Solomon Eyal Shimony & Amos Beimel - 2009 - Artificial Intelligence 173 (12-13):1150-1153.
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  • Complexity results for explanations in the structural-model approach.Thomas Eiter & Thomas Lukasiewicz - 2004 - Artificial Intelligence 154 (1-2):145-198.
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  • Relational preference rules for control.Ronen I. Brafman - 2011 - Artificial Intelligence 175 (7-8):1180-1193.
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  • When Can Predictive Brains be Truly Bayesian?Mark Blokpoel, Johan Kwisthout & Iris van Rooij - 2012 - Frontiers in Psychology 3.
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