Motivation Learning in Mind Model CAM
Affiliation(s)
1 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. 2 University of Chinese Academy of Sciences, Beijing, China. 3 School of Information Science and Engineering, Qufu Normal University, Rizhao, China.
1 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. 2 University of Chinese Academy of Sciences, Beijing, China. 3 School of Information Science and Engineering, Qufu Normal University, Rizhao, China.
ABSTRACT
Motivation learning aims to create abstract motivations and related goals. It is one of the high-level cognitive functions in Consciousness And Memory model (CAM). This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process. The simulation of cyborg rat maze search shows that the motivation learning algorithm can adapt agents’ behavior in response to dynamic environment.
Motivation learning aims to create abstract motivations and related goals. It is one of the high-level cognitive functions in Consciousness And Memory model (CAM). This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process. The simulation of cyborg rat maze search shows that the motivation learning algorithm can adapt agents’ behavior in response to dynamic environment.
Cite this paper
Shi, Z. , Ma, G. , Yang, X. and Lu, C. (2015) Motivation Learning in Mind Model CAM. International Journal of Intelligence Science, 5, 63-71. doi: 10.4236/ijis.2015.52006.
Shi, Z. , Ma, G. , Yang, X. and Lu, C. (2015) Motivation Learning in Mind Model CAM. International Journal of Intelligence Science, 5, 63-71. doi: 10.4236/ijis.2015.52006.
References
[1] Shi, Z.Z., Wang, X.F., Chen, L.M. and Zhang, Z.X. (2010) A Mind Model CAM—Consciousness and Memory Model. Proceedings of 7th International Conference on Cognitive Science, Beijing, 17-20 August 2010, 226-227. [2] Mook, D.G. (1987) Motivation: The Organization of Action. W. W. Norton and Company, Inc., New York. [3] Maslow, A.H. (1943) A Theory of Human Motivation. Psychological Review, 50, 370-396.
http://dx.doi.org/10.1037/h0054346 [4] Green, R.G., Beatty, W.W. and Arkin, R.M. (1984) Human Motivation: Physiological, Behavioral and Social Approaches. Allyn and Bacon, Inc., Boston. [5] Merrick, K. (2007) Modelling Motivation for Experience-Based Attention Focus in Reinforcement Learning. Ph.D. Thesis, The University of Sydney, Sydney. [6] Starzyk, J.A. (2012) Motivated Learning for Computational Intelligence. In: Information Resources Management Association, Ed., Machine Learning: Concepts, Methodologies, Tools and Applications (3 Volumes), IGI Global, Hershey, 120-146. [7] Oudeyer, P.-Y., Kaplan, F. and Hafner, V. (2007) Intrinsic Motivation Systems for Autonomous Mental Development. IEEE Transactions on Evolutionary Computation, 11, 265-286.
http://dx.doi.org/10.1109/TEVC.2006.890271 [8] Berlyne, D.E. (1960) Conflict, Arousal and Curiosity. McGraw-Hill, New York.
http://dx.doi.org/10.1037/11164-000 [9] Schmidhuber, J. (1991) Curious Model—Building Control Systems. Proceedings of the International Joint Conference on Neural Networks, Singapore City, 18-21 November 1991, 1458-1463. [10] Markou, M. and Singh, S. (2003) Novelty Detection: A Review—Part 1: Statistical Approaches. Signal Processing, 83, 2481-2497. http://dx.doi.org/10.1016/j.sigpro.2003.07.018 [11] Markou, M. and Singh, S. (2003) Novelty Detection: A Review—Part 2: Neural Network Based Approaches. Signal Processing, 83, 2499-2521. http://dx.doi.org/10.1016/j.sigpro.2003.07.019 [12] Shi, Z.Z. (2011) Intelligence Science. World Scientific, Singapore City. [13] Cox, M. (2005) Metacognition in Computation: A Selected Research Review. Artificial Intelligence, 169, 104-141. http://dx.doi.org/10.1016/j.artint.2005.10.009 [14] Laird, J.E., Newell, A. and Rosenbloom, P.S. (1987) SOAR: An Architecture for General Intelligence. Artificial Intelligence, 33, 1-64. http://dx.doi.org/10.1016/0004-3702(87)90050-6 [15] Birnbaum, L., Collins, G., Brand, M., Freed, M., Krulwich, B. and Pryor, L. (1991) A Model-Based Approach to the Construction of Adaptive Case-Based Planning Systems. Proceedings of the DARPA Case-Based Reasoning Workshop, Morgan Kaufmann, San Mateo, 215-224. [16] Cox, M.T. and Ram, A. (1999) Introspective Multistrategy Learning: On the Construction of Learning Strategies. Artificial Intelligence, 112, 1-55. http://dx.doi.org/10.1016/S0004-3702(99)00047-8 [17] Leake, D.B. and Wilson, M. (2008) Extending Introspective Learning from Self-Models. In: Cox, M.T. and Raja, A., Eds., Metareasoning: Thinking about Thinking, AAAI Press, Palo Alto, 143-146. [18] Eriksen, C. and St James, J. (1986) Visual Attention within and around the Field of Focal Attention: A Zoom Lens Model. Perception & Psychophysics, 40, 225-240. http://dx.doi.org/10.3758/BF03211502 [19] Baddeley, A. (2000) The Episodic Buffer: A New Component of Working Memory? Trends in Cognitive Sciences, 4, 417-423. http://dx.doi.org/10.1016/S1364-6613(00)01538-2 [20] Shi, Z.Z., Wang, X.F. and Yue, J.P. (2011) Cognitive Cycle in Mind Model CAM. International Journal of Intelligence Science, 1, 25-34. http://dx.doi.org/10.4236/ijis.2011.12004 [21] Shi, Z.Z., Zhang, J.H., Yue, J.P. and Qi, B.Y. (2013) A Motivational System for Mind Model CAM. AAAI Symposium on Integrated Cognition, Virginia, 15-17 November 2013, 79-86. [22] Saunders, R. and Gero, J.S. (2002) Curious Agents and Situated Design Evaluations. In: Gero, J.S. and Brazier, F.M.T., Eds., Agents in Design, Key Centre of Design Computing and Cognition, University of Sydney, Sydney, 133-149. [23] Li, Q.Y., Shi, J. and Shi, Z.Z. (2005) A Model of Attention-Guided Visual Sparse Coding. Proceedings of IEEE International Conference on Cognitive Informatics, Irvine, 8-10 August 2005, 120-125.
[1] Shi, Z.Z., Wang, X.F., Chen, L.M. and Zhang, Z.X. (2010) A Mind Model CAM—Consciousness and Memory Model. Proceedings of 7th International Conference on Cognitive Science, Beijing, 17-20 August 2010, 226-227. [2] Mook, D.G. (1987) Motivation: The Organization of Action. W. W. Norton and Company, Inc., New York. [3] Maslow, A.H. (1943) A Theory of Human Motivation. Psychological Review, 50, 370-396.
http://dx.doi.org/10.1037/h0054346 [4] Green, R.G., Beatty, W.W. and Arkin, R.M. (1984) Human Motivation: Physiological, Behavioral and Social Approaches. Allyn and Bacon, Inc., Boston. [5] Merrick, K. (2007) Modelling Motivation for Experience-Based Attention Focus in Reinforcement Learning. Ph.D. Thesis, The University of Sydney, Sydney. [6] Starzyk, J.A. (2012) Motivated Learning for Computational Intelligence. In: Information Resources Management Association, Ed., Machine Learning: Concepts, Methodologies, Tools and Applications (3 Volumes), IGI Global, Hershey, 120-146. [7] Oudeyer, P.-Y., Kaplan, F. and Hafner, V. (2007) Intrinsic Motivation Systems for Autonomous Mental Development. IEEE Transactions on Evolutionary Computation, 11, 265-286.
http://dx.doi.org/10.1109/TEVC.2006.890271 [8] Berlyne, D.E. (1960) Conflict, Arousal and Curiosity. McGraw-Hill, New York.
http://dx.doi.org/10.1037/11164-000 [9] Schmidhuber, J. (1991) Curious Model—Building Control Systems. Proceedings of the International Joint Conference on Neural Networks, Singapore City, 18-21 November 1991, 1458-1463. [10] Markou, M. and Singh, S. (2003) Novelty Detection: A Review—Part 1: Statistical Approaches. Signal Processing, 83, 2481-2497. http://dx.doi.org/10.1016/j.sigpro.2003.07.018 [11] Markou, M. and Singh, S. (2003) Novelty Detection: A Review—Part 2: Neural Network Based Approaches. Signal Processing, 83, 2499-2521. http://dx.doi.org/10.1016/j.sigpro.2003.07.019 [12] Shi, Z.Z. (2011) Intelligence Science. World Scientific, Singapore City. [13] Cox, M. (2005) Metacognition in Computation: A Selected Research Review. Artificial Intelligence, 169, 104-141. http://dx.doi.org/10.1016/j.artint.2005.10.009 [14] Laird, J.E., Newell, A. and Rosenbloom, P.S. (1987) SOAR: An Architecture for General Intelligence. Artificial Intelligence, 33, 1-64. http://dx.doi.org/10.1016/0004-3702(87)90050-6 [15] Birnbaum, L., Collins, G., Brand, M., Freed, M., Krulwich, B. and Pryor, L. (1991) A Model-Based Approach to the Construction of Adaptive Case-Based Planning Systems. Proceedings of the DARPA Case-Based Reasoning Workshop, Morgan Kaufmann, San Mateo, 215-224. [16] Cox, M.T. and Ram, A. (1999) Introspective Multistrategy Learning: On the Construction of Learning Strategies. Artificial Intelligence, 112, 1-55. http://dx.doi.org/10.1016/S0004-3702(99)00047-8 [17] Leake, D.B. and Wilson, M. (2008) Extending Introspective Learning from Self-Models. In: Cox, M.T. and Raja, A., Eds., Metareasoning: Thinking about Thinking, AAAI Press, Palo Alto, 143-146. [18] Eriksen, C. and St James, J. (1986) Visual Attention within and around the Field of Focal Attention: A Zoom Lens Model. Perception & Psychophysics, 40, 225-240. http://dx.doi.org/10.3758/BF03211502 [19] Baddeley, A. (2000) The Episodic Buffer: A New Component of Working Memory? Trends in Cognitive Sciences, 4, 417-423. http://dx.doi.org/10.1016/S1364-6613(00)01538-2 [20] Shi, Z.Z., Wang, X.F. and Yue, J.P. (2011) Cognitive Cycle in Mind Model CAM. International Journal of Intelligence Science, 1, 25-34. http://dx.doi.org/10.4236/ijis.2011.12004 [21] Shi, Z.Z., Zhang, J.H., Yue, J.P. and Qi, B.Y. (2013) A Motivational System for Mind Model CAM. AAAI Symposium on Integrated Cognition, Virginia, 15-17 November 2013, 79-86. [22] Saunders, R. and Gero, J.S. (2002) Curious Agents and Situated Design Evaluations. In: Gero, J.S. and Brazier, F.M.T., Eds., Agents in Design, Key Centre of Design Computing and Cognition, University of Sydney, Sydney, 133-149. [23] Li, Q.Y., Shi, J. and Shi, Z.Z. (2005) A Model of Attention-Guided Visual Sparse Coding. Proceedings of IEEE International Conference on Cognitive Informatics, Irvine, 8-10 August 2005, 120-125.