Most probable explanations in Bayesian networks: Complexity and tractability
International Journal of Approximate Reasoning
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In cognitive science, natural cognitive processes are generally conceptualized as computational processes: they serve to transform sensory and mental inputs into mental and action outputs. At the highest level of abstraction, computational models of cognitive processes aim at specifying the computational problem computed by the process under study. Because computational problems are realistic cognitive models only insofar as they can plausibly be computed by the human brain given its limited resources for computation, computational tractability provides a useful constraint on cognitive models. In this paper, we consider the particular benefits of the parameterized complexity framework for identifying sources of intractability in cognitive models. We review existing applications of the parameterized framework to this end in the domains of perception, action and higher cognition. We further identify important opportunities and challenges for future research. These include the development of new methods for complexity analyses specifically tailored to the reverse engineering perspective underlying cognitive science.