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Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence.
Strips: A new approach to the application of theorem proving to problem solving
Richard E Fikes and Nils J Nilsson · 1971
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John McCarthy · 1978
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Hendrik P Barendregt et al · 1984
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Refinement types for ml
Tim Freeman and Frank Pfenning · 1991
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Knowledge-based artificial neural networks
Geoffrey G Towell and Jude W Shavlik · 1994
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The birth of Prolog
Alain Colmerauer and Philippe Roussel · 1996
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
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Reasoning about Noisy Sensors and Effectors in the Situation Calculus
Fahiem Bacchus, Joseph Y. Halpern, and Hector J. Levesque · 1999
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Abducing through Negation as Failure: Stable Models within the Independent Choice Logic
David Poole · 2000
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Reasoning about Uncertainty
Joseph Halpern · 2003
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Owl web ontology language overview
Deborah L McGuinness, Frank Van Harmelen, et al · 2004
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PPDDL1.0: The language for the probabilistic part of IPC-4
Håkan LS Younes and Michael L Littman · 2004
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Combining reinforcement learning with symbolic planning
Matthew Grounds and Daniel Kudenko · 2005
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The fast downward planning system
Malte Helmert · 2006
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An Unified Cognitive Architecture for Physical Agents
Patrick Langley and Dongkyu Choi · 2006
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BLOG: Probabilistic Models with Unknown Objects
Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, and Andrey Kolobov · 2006
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Markov Logic Networks
Matthew Richardson and Pedro Domingos · 2006
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First Steps Towards Natural Human-Like HRI
Matthias Scheutz, Paul Schermerhorn, James Kramer, and David Anderson · 2007
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Plan-based reward shaping for reinforcement learning
Marek Grzes and Daniel Kudenko · 2008
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Probabilistic Reasoning with Answer Sets
Chitta Baral, Michael Gelfond, and Nelson Rushton · 2009
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A Language for Relational Decision Theory
Aniruddh Nath and Pedro Domingos · 2009
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DTProbLog: A Decision-Theoretic Probabilistic Prolog
Guy Van den Broeck, Ingo Thon, Martijn van Otterlo, and Luc De Raedt · 2010
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Refinement types for logical frameworks
William Lovas · 2010
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Refinement types for logical frameworks and their interpretation as proof irrelevance
Frank Pfenning and William Lovas · 2010
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Symbolic dynamic programming for first-order pomdps
Scott Sanner and Kristian Kersting · 2010
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Relational dynamic influence diagram language (RDDL): Language description
Scott Sanner · 2010
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A switching planner for combined task and observation planning
Moritz Göbelbecker, Charles Gretton, and Richard Dearden · 2011
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Answer Set Solving in Practice, Synthesis Lectures on Artificial Intelligence and Machine Learning
Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub · 2012
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Model Checking with Probabilistic Tabled Logic Programming
Andrey Gorlin, C. R. Ramakrishnan, and Scott A. Smolka · 2012
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The Soar Cognitive Architecture
John E Laird · 2012
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Is someone in this office available to help me?
Stephanie Rosenthal, Manuela Veloso, and Anind K Dey · 2012
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Using the web to interactively learn to find objects
Mehdi Samadi, Thomas Kollar, and Manuela Veloso · 2012
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Using plan-based reward shaping to learn strategies in starcraft: Broodwar
Kyriakos Efthymiadis and Daniel Kudenko · 2013
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Some Properties of System Descriptions of A L d AL_{d}
Michael Gelfond and Daniela Inclezan · 2013
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Plog: Its algorithms and applications
Weijun Zhu · 2013
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The atomic components of thought
John R Anderson and Christian J Lebiere · 2014
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Knowledge representation, reasoning, and the design of intelligent agents: The answer-set programming approach
Michael Gelfond and Yulia Kahl · 2014
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An interactive approach for situated task specification through verbal instructions
Cetin Mericli, Steven D Klee, Jack Paparian, and Manuela Veloso · 2014
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Markov Decision Processes.: Discrete Stochastic Dynamic Programming
Martin L Puterman · 2014
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A theory of intentions for intelligent agents
Justin Blount, Michael Gelfond, and Marcello Balduccini · 2015
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Inference and Learning in Probabilistic Logic Programs using Weighted Boolean Formulas
Dann Fierens, Guy Van Den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, and Luc De Raedt · 2015
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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PEORL: integrating symbolic planning and hierarchical reinforcement learning for robust decision-making
Fangkai Yang, Daoming Lyu, Bo Liu, and Steven Gustafson · 2018
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Augmenting knowledge through statistical, goal-oriented human-robot dialog
Saeid Amiri, Sujay Bajracharya, Cihangir Goktolgal, Jesse Thomason, and Shiqi Zhang · 2019
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Explainable agents and robots: Results from a systematic literature review
Sule Anjomshoae, Amro Najjar, Davide Calvaresi, and Kary Framling · 2019
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P-log: refinement and a new coherency condition
Evgenii Balai, Michael Gelfond, and Yuanlin Zhang · 2019
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LTL and beyond: Formal languages for reward function specification in reinforcement learning
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Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, et al · 2015
Cited alongside, same era.
Probabilistic Logic Programming Concepts
Luc De Raedt and Angelika Kimmig · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Learning to interpret natural language commands through human-robot dialog
Jesse Thomason, Shiqi Zhang, Raymond Mooney, and Peter Stone · 2015
Cited alongside, same era.
CORPP: commonsense reasoning and probabilistic planning, as applied to dialog with a mobile robot
Shiqi Zhang and Peter Stone · 2015
Cited alongside, same era.
Alberto Camacho, Rodrigo Toro Icarte, Toryn Q Klassen, Richard Anthony Valenzano, and Sheila A McIlraith · 2019
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Search on the replay buffer: Bridging planning and reinforcement learning
Ben Eysenbach, Russ R Salakhutdinov, and Sergey Levine · 2019
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Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
AD Garcez, M Gori, LC Lamb, L Serafini, M Spranger, and SN Tran · 2019
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An introduction to the planning domain definition language
Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, and Christian Muise · 2019
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Open-world reasoning for service robots
Yuqian Jiang, Nick Walker, Justin Hart, and Peter Stone · 2019
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SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning
Daoming Lyu, Fangkai Yang, Bo Liu, and Steven Gustafson · 2019
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Online Learning of Feed-Forward Models for Task-Space Variable Impedance Control
Michael Mathew, Saif Sidhik, Mohan Sridharan, Morteza Azad, Akinobu Hayashi, and Jeremy Wyatt · 2019
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Explanations in Artificial Intelligence: Insights from the Social Sciences
Tim Miller · 2019
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Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning on Robots
Tiago Mota and Mohan Sridharan · 2019
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Integrating Non-monotonic Logical Reasoning and Inductive Learning With Deep Learning for Explainable Visual Question Answering
Heather Riley and Mohan Sridharan · 2019
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Finding Optimal Feasible Global Plans for Multiple Teams of Heterogeneous Robots using Hybrid Reasoning: An Application to Cognitive Factories
Zeynep Saribatur, Volkan Patoglu, and Esra Erdem · 2019
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Towards a Theory of Explanations for Human-Robot Collaboration
Mohan Sridharan and Benjamin Meadows · 2019
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Reba: A refinement-based architecture for knowledge representation and reasoning in robotics
Mohan Sridharan, Michael Gelfond, Shiqi Zhang, and Jeremy Wyatt · 2019
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Bridging commonsense reasoning and probabilistic planning via a probabilistic action language
Yi Wang, Shiqi Zhang, and Joohyung Lee · 2019
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Faster and safer training by embedding high-level knowledge into deep reinforcement learning
Haodi Zhang, Zihang Gao, Yi Zhou, Hao Zhang, Kaishun Wu, and Fangzhen Lin · 2019
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Learning and reasoning for robot sequential decision making under uncertainty
Saeid Amiri, Mohammad Shokrolah Shirazi, and Shiqi Zhang · 2020
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Learning compact models for planning with exogenous processes
Rohan Chitnis and Tomás Lozano-Pérez · 2020
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Induction of subgoal automata for reinforcement learning
Daniel Furelos-Blanco, Mark Law, Alessandra Russo, Krysia Broda, and Anders Jonsson · 2020
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Simultaneously learning transferable symbols and language groundings from perceptual data for instruction following
Nakul Gopalan, Eric Rosen, George Konidaris, and Stefanie Tellex · 2020
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Symbolic plans as high-level instructions for reinforcement learning
León Illanes, Xi Yan, Rodrigo Toro Icarte, and Sheila A McIlraith · 2020
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Integrated task and motion planning
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What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration
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Guiding robot exploration in reinforcement learning via automated planning
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Answer me this: Constructing Disambiguation Queries for Explanation Generation in Robotics
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Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics
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Abstraction for Non-ground Answer Set Programs
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Towards a Framework for Changing-Contact Manipulation Tasks
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Neuro-symbolic artificial intelligence: The state of the art
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Reward machines: Exploiting reward function structure in reinforcement learning
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