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  1. Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.
    Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...)
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  • Multiple representations of space underlying behavior.Israel Lieblich & Michael A. Arbib - 1982 - Behavioral and Brain Sciences 5 (4):627-640.
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  • A maze in graphs.Christopher K. Riesbeck - 1982 - Behavioral and Brain Sciences 5 (4):648-648.
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  • Intention without representation.Peter Wallis - 2004 - Philosophical Psychology 17 (2):209-223.
    A mechanism for planning ahead would appear to be essential to any creature with more than insect level intelligence. In this paper it is shown how planning, using full means-ends analysis, can be had while avoiding the so called symbol grounding problem. The key role of knowledge representation in intelligence has been acknowledged since at least the enlightenment, but the advent of the computer has made it possible to explore the limits of alternate schemes, and to explore the nature of (...)
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  • Counterfactuals.Matthew L. Ginsberg - 1986 - Artificial Intelligence 30 (1):35-79.
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  • Automated discourse generation using discourse structure relations.Eduard H. Hovy - 1993 - Artificial Intelligence 63 (1-2):341-385.
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  • Using action-based hierarchies for real-time diagnosis.David Ash & Barbara Hayes-Roth - 1996 - Artificial Intelligence 88 (1-2):317-347.
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  • Put my galakmid Coin into the dispenser and kick it: Computational linguistics and theorem proving in a computer game. [REVIEW]Alexander Koller, Ralph Debusmann, Malte Gabsdil & Kristina Striegnitz - 2004 - Journal of Logic, Language and Information 13 (2):187-206.
    We combine state-of-the-art techniques from computational linguisticsand theorem proving to build an engine for playing text adventures,computer games with which the player interacts purely through naturallanguage. The system employs a parser for dependency grammar and ageneration system based on TAG, and has components for resolving andgenerating referring expressions. Most of these modules make heavy useof inferences offered by a modern theorem prover for descriptionlogic. Our game engine solves some problems inherent in classical textadventures, and is an interesting test case for (...)
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  • Learning hierarchical task network domains from partially observed plan traces.Hankz Hankui Zhuo, Héctor Muñoz-Avila & Qiang Yang - 2014 - Artificial Intelligence 212 (C):134-157.
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  • A validation-structure-based theory of plan modification and reuse.Subbarao Kambhampati & James A. Hendler - 1992 - Artificial Intelligence 55 (2-3):193-258.
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  • Acquiring search-control knowledge via static analysis.Oren Etzioni - 1993 - Artificial Intelligence 62 (2):255-301.
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  • The special nature of spatial information.Michael Potegal - 1982 - Behavioral and Brain Sciences 5 (4):647-648.
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  • Looking for nodes and edges.Arnold Trehub - 1982 - Behavioral and Brain Sciences 5 (4):650-651.
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  • A planning system for robot construction tasks.Scott Elliott Fahlman - 1974 - Artificial Intelligence 5 (1):1-49.
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  • Computational research on interaction and agency.Philip E. Agre - 1995 - Artificial Intelligence 72 (1-2):1-52.
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  • Lost in Chelm: Maladaptive behavior in an adaptive model.Stephen Kaplan - 1982 - Behavioral and Brain Sciences 5 (4):643-644.
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  • A structural theory of explanation-based learning.Oren Etzioni - 1993 - Artificial Intelligence 60 (1):93-139.
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  • Probabilistic logic revisited.Nils J. Nilsson - 1993 - Artificial Intelligence 59 (1-2):39-42.
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  • Human spatial learning.Kristina Hooper - 1982 - Behavioral and Brain Sciences 5 (4):642-643.
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  • The cognitive map must be a separate module.Benjamin Kuipers - 1982 - Behavioral and Brain Sciences 5 (4):645-646.
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  • What spaces? What subjects?Jean Pailhous & Patrick Peruch - 1982 - Behavioral and Brain Sciences 5 (4):646-647.
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  • Towards a computational theory of cognitive maps.Wai K. Yeap - 1988 - Artificial Intelligence 34 (3):297-360.
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  • Finding optimal derivation strategies in redundant knowledge bases.Russell Greiner - 1991 - Artificial Intelligence 50 (1):95-115.
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  • Exploratory behavior without novelty drive?Arthur I. Karshmer, Derek Partridge & Victor Johnson - 1982 - Behavioral and Brain Sciences 5 (4):644-645.
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  • Rethinking autonomy.Richard Alterman - 2000 - Minds and Machines 10 (1):15-30.
    This paper explores the assumption of autonomy. Several arguments are presented against the assumption of runtime autonomy as a principle of design for artificial intelligence systems. The arguments vary from being theoretical, to practical, and to analytic. The latter parts of the paper focus on one strategy for building non-autonomous systems (the practice view). One critical theme is that intelligence is not located in the system alone, it emerges from a history of interactions among user, builder, and designer over a (...)
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  • A theory and methodology of inductive learning.Ryszard S. Michalski - 1983 - Artificial Intelligence 20 (2):111-161.
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  • Continuous case-based reasoning.A. Ram & J. C. Santamaría - 1997 - Artificial Intelligence 90 (1-2):25-77.
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  • Representations of the environment, multiple brain maps, and control systems.Charles M. Butter - 1982 - Behavioral and Brain Sciences 5 (4):640-641.
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  • Computational Hullianism.John W. Moore - 1982 - Behavioral and Brain Sciences 5 (4):646-646.
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  • Standards for neural modeling.Jerome A. Feldman & David Zipser - 1982 - Behavioral and Brain Sciences 5 (4):642-642.
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  • Axioms of causal relevance.David Galles & Judea Pearl - 1997 - Artificial Intelligence 97 (1-2):9-43.
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  • Model-lite planning: Case-based vs. model-based approaches.Hankz Hankui Zhuo & Subbarao Kambhampati - 2017 - Artificial Intelligence 246 (C):1-21.
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  • A chess combination program which uses plans.Jacques Pitrat - 1977 - Artificial Intelligence 8 (3):275-321.
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  • Automatically generating abstractions for planning.Craig A. Knoblock - 1994 - Artificial Intelligence 68 (2):243-302.
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  • Smart office robot collaboration based on multi-agent programming.F. Mizoguchi, H. Nishiyama, H. Ohwada & H. Hiraishi - 1999 - Artificial Intelligence 114 (1-2):57-94.
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  • Modeling simultaneous actions and continuous processes.Gary G. Hendrix - 1973 - Artificial Intelligence 4 (3-4):145-180.
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  • BankXX: Supporting legal arguments through heuristic retrieval. [REVIEW]Edwina L. Rissland, David B. Skalak & M. Timur Friedman - 1996 - Artificial Intelligence and Law 4 (1):1-71.
    The BankXX system models the process of perusing and gathering information for argument as a heuristic best-first search for relevant cases, theories, and other domain-specific information. As BankXX searches its heterogeneous and highly interconnected network of domain knowledge, information is incrementally analyzed and amalgamated into a dozen desirable ingredients for argument (called argument pieces), such as citations to cases, applications of legal theories, and references to prototypical factual scenarios. At the conclusion of the search, BankXX outputs the set of argument (...)
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  • Analyzing generalized planning under nondeterminism.Vaishak Belle - 2022 - Artificial Intelligence 307 (C):103696.
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  • Speculative plan execution for information gathering.Greg Barish & Craig A. Knoblock - 2008 - Artificial Intelligence 172 (4-5):413-453.
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  • Explanation-based learning:A problem solving perspective.Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Daniel R. Kuokka, Oren Etzioni & Yolanda Gil - 1989 - Artificial Intelligence 40 (1-3):63-118.
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  • O-Plan: The open planning architecture.Ken Currie & Austin Tate - 1991 - Artificial Intelligence 52 (1):49-86.
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  • A unified framework for explanation-based generalization of partially ordered and partially instantiated plans.Subbarao Kambhampati & Smadar Kedar - 1994 - Artificial Intelligence 67 (1):29-70.
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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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  • Inductive learning of structural descriptions.Thomas G. Dietterich & Ryszard S. Michalski - 1981 - Artificial Intelligence 16 (3):257-294.
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  • Macro-operators: A weak method for learning.Richard E. Korf - 1985 - Artificial Intelligence 26 (1):35-77.
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  • Dynamic backward reasoning systems.Antoni Ligęza - 1990 - Artificial Intelligence 43 (2):127-152.
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  • SOAR: An architecture for general intelligence.John E. Laird, Allen Newell & Paul S. Rosenbloom - 1987 - Artificial Intelligence 33 (1):1-64.
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  • Quantitative results concerning the utility of explanation-based learning.Steven Minton - 1990 - Artificial Intelligence 42 (2-3):363-391.
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  • Active Learning: Approaches and Issues.T. R. Chaudhur & L. G. C. Hamey - 1997 - Journal of Intelligent Systems 7 (3-4):205-244.
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  • A new representation and associated algorithms for generalized planning.Siddharth Srivastava, Neil Immerman & Shlomo Zilberstein - 2011 - Artificial Intelligence 175 (2):615-647.
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