Abstract
A key goal in designing an artificial intelligence capable of performing complex tasks is a mechanism that allows it to efficiently choose appropriate and relevant actions in a variety of situations and contexts. Nowhere is this more obvious than in the case of building a general intelligence, where the contextual choice and application of actions must be done in the presence of large numbers of alternatives, both subtly and obviously distinct from each other. We present a framework for action selection based on the concurrent activity of multiple forward and inverse models. A key characteristic of the proposed system is the use of simulation to choose an action: the system continuously simulates the external states of the world (proximal and distal) by internally emulating the activity of its sensors, adopting the same decision process as if it were actually operating in the world, and basing subsequent choice of action on the outcome of such simulations. The work is part of our larger effort to create new observation-based machine learning techniques. We describe our approach, an early implementation, and an evaluation in a classical AI problem-solving domain: the Sokoban puzzle.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Balleine, B.W., Dickinson, A.: Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology 37(4-5), 407–419 (1998)
Cassimatis, N.L., Trafton, J.G., Bugajska, M.D., Schultz, A.C.: Integrating cognition, perception and action through mental simulation in robots. Robotics and Autonomous Systems 49(1-2), 13–23 (2004)
Chella, A., Lebiere, C., Noelle, D., Samsonovich, A.: T on a roadmap to biologically inspired cognitive agents. In: Biologically Inspired Cognitive Architectures. Frontiers in Artificial Intelligence and Applications, vol. 233, pp. 453–460 (2011)
Damasio, A.R., Everitt, B.J., Bishop, D.: The somatic marker hypothesis and the possible functions of the prefrontal cortex [and discussion]. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 351(1346), 1413–1420 (1996)
Dindo, H., Chella, A., La Tona, G., Vitali, M., Nivel, E., Thórisson, K.R.: Learning Problem Solving Skills from Demonstration: An Architectural Approach. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds.) AGI 2011. LNCS, vol. 6830, pp. 194–203. Springer, Heidelberg (2011)
Dor, D., Zwick, U.: Sokoban and other motion planning problems. Computational Geometry 13(4), 215–228 (1999)
Gigliotta, O., Pezzulo, G., Nolfi, S.: Evolution of a predictive internal model in an embodied and situated agent. Theory in Biosciences (2011)
Grush, R.: The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences 27(03), 377–396 (2004)
Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences 6(6), 242–247 (2002)
Jeannerod, M.: Neural simulation of action: A unifying mechanism for motor cognition. NeuroImage 14, S103–S109 (2001)
Junghanns, A., Schaeffer, J.: Sokoban: Enhancing general single-agent search methods using domain knowledge. Artificial Intelligence 129(1-2), 219–251 (2001)
Kawato, M.: Internal models for motor control and trajectory planning. Current Opinion in Neurobiology 9, 718–727 (1999)
Pezzulo, G.: Coordinating with the future: the anticipatory nature of representation. Minds and Machines 18(2), 179–225 (2008)
Pezzulo, G.: A Study of Off-Line Uses of Anticipation. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS (LNAI), vol. 5040, pp. 372–382. Springer, Heidelberg (2008)
Pezzulo, G., Butz, M.V., Castelfranchi, C.: The Anticipatory Approach: Definitions and Taxonomies. In: Pezzulo, G., Butz, M.V., Castelfranchi, C., Falcone, R. (eds.) The Challenge of Anticipation. LNCS (LNAI), vol. 5225, pp. 23–43. Springer, Heidelberg (2008)
Suddendorf, T., Corballis, M.C.: The evolution of foresight: What is mental time travel, and is it unique to humans? Behavioral and Brain Sciences 30(03), 299–313 (2007)
Szpunar, K.: Episodic future thought an emerging concept. Perspectives on Psychological Science 5(2), 142–162 (2010)
Thórisson, K.R.: From constructionist to constructivist A.I. Keynote. In: AAAI Fall Symposium Series: Biologically Inspired Cognitive Architectures, Washington D.C. Also available as AAAI Tech. Report FS-09-01, pp. 175–183. AAAI Press, Menlo Park (2009)
Toussaint, M.: A sensorimotor map: Modulating lateral interactions for anticipation and planning. Neural Computation 18(5), 1132–1155 (2006)
Wolpert, D., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11(7-8), 1317–1329 (1998)
Ziemke, T., Jirenhed, D.A., Hesslow, G.: Internal simulation of perception: a minimal neuro-robotic model. Neurocomputing 68, 85–104 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dindo, H., La Tona, G., Nivel, E., Pezzulo, G., Chella, A., Thórisson, K.R. (2013). Simulation and Anticipation as Tools for Coordinating with the Future. In: Chella, A., Pirrone, R., Sorbello, R., Jóhannsdóttir, K. (eds) Biologically Inspired Cognitive Architectures 2012. Advances in Intelligent Systems and Computing, vol 196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34274-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-34274-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34273-8
Online ISBN: 978-3-642-34274-5
eBook Packages: EngineeringEngineering (R0)