Learning acceptable windows of contingency

By learning a range of possible times over which the effect of an action can take place, a robot can reason more effectively about causal and contingent relationships in the world. An algorithm is presented for learning the interval of possible times during which a response to an action can take place. The algorithm was implemented on a physical robot for the domains of visual self-recognition and auditory social-partner recognition. The environment model assumes that natural environments generate Poisson distributions of random events at all scales. A linear-time algorithm called Poisson threshold learning can generate a threshold T that provides an arbitrarily small rate of background events λ (T), if such a threshold exists for the specified error rate.

[1]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[2]  B. Harshbarger.  An Introduction to Probability Theory and its Applications, Volume I , 1958 .

[3]  W. Feller,et al.  An Introduction to Probability Theory and Its Applications, Vol. 1 , 1967 .

[4]  G. Gallup.  Chimpanzees: Self-Recognition , 1970, Science.

[5]  B. Amsterdam,et al.  Mirror self-image reactions before age two. , 1972, Developmental psychobiology.

[6]  James F. Allen.  An Interval-Based Representation of Temporal Knowledge , 1981, IJCAI.

[7]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[8]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[9]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[10]  S. Carey,et al.  Whose gaze will infants follow? Features that elicit gaze-following in twelve-month-olds , 1996 .

[11]  S. Carey,et al.  Whose gaze will infants follow? The elicitation of gaze-following in 12-month-olds , 1998 .

[12]  J. Nadel,et al.  Expectancies for social contingency in 2‐month‐olds , 1999 .

[13]  Patrice D. Tremoulet,et al.  Perceptual causality and animacy , 2000, Trends in Cognitive Sciences.

[14]  D. Reiss,et al.  Mirror self-recognition in the bottlenose dolphin: A case of cognitive convergence , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[15]  James L. McClelland,et al.  Autonomous Mental Development by Robots and Animals , 2001, Science.

[16]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[17]  Paul R. Cohen,et al.  Learning effects of robot actions using temporal associations , 2002, Proceedings 2nd International Conference on Development and Learning. ICDL 2002.

[18]  G. Gallup,et al.  The mirror test , 2002 .

[19]  C. Allen,et al.  The Cognitive Animal: Empirical and Theoretical Perspectives on Animal Cognition , 2002 .

[20]  Paul M. Fitzpatrick,et al.  Developmentally deep perceptual system for a humanoid robot , 2003 .

[21]  Minoru Asada,et al.  Cross-anchoring for binding tactile and visual sensations via unique association through self-perception , 2004 .

[22]  Brian Scassellati,et al.  Motion-based robotic self-recognition , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[23]  Minoru Asada,et al.  Is it my body? Body extraction from uninterpreted sensory data based on the invariance of multiple sensory attributes , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[24]  Javier R. Movellan,et al.  Infomax Control as a Model of Real Time Behavior: Theory and Application to the Detection of Social Contingency , 2005 .

[25]  Brian Scassellati,et al.  Using context and sensory data to learn first and second person pronouns , 2006, HRI '06.