Construction of an Internal Predictive Model by Event Anticipation

Capdepuy, Philippe; Polani, Daniel; Nehaniv, Chrystopher L.

doi:10.1007/978-3-540-74262-3_12

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Sensorimotor correlations could be further differentiated motor dependently, as proposed elsewhere (Möller & Schenck, 2008;Olsson, 2006;Pierce & Kuipers, 1997). The information theoretic concepts proposed by Capdepuy, Polani, and Nehaniv (2007) may be used to identify motor-relevant sensory information and information redundancies. In particular, Capdepuy et al (2007) use an information gain concept to identify correlated events based on modeled certainty in time-delay and contingency.…”

Section: Discussionmentioning

confidence: 99%

“…The information theoretic concepts proposed by Capdepuy, Polani, and Nehaniv (2007) may be used to identify motor-relevant sensory information and information redundancies. In particular, Capdepuy et al (2007) use an information gain concept to identify correlated events based on modeled certainty in time-delay and contingency. Thus, events with high predictive information content were identified and used for the construction of an internal model.…”

Section: Discussionmentioning

confidence: 99%

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Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior

2010

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This article investigates how a motivational module can drive an animat to learn a sensorimotor cognitive map and use it to generate flexible goal-directed behavior. Inspired by the rat's hippocampus and neighboring areas, the time growing neural gas (TGNG) algorithm is used, which iteratively builds such a map by means of temporal Hebbian learning. The algorithm is combined with a motivation module, which activates goals, priorities, and consequent activity gradients in the developing cognitive map for the self-motivated control of behavior. The resulting motivated TGNG thus combines a neural cognitive map learning process with top-down, self-motivated, anticipatory behavior control mechanisms. While the algorithms involved are kept rather simple, motivated TGNG displays several emergent behavioral patterns, self-sustainment, and reliable latent learning. We conclude that motivated TGNG constitutes a solid basis for future studies on self-motivated cognitive map learning, on the design of further enhanced systems with additional cognitive modules, and on the realization of highly adaptive, interactive, goal-directed, cognitive systems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior

2010

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“…Philippe Capdepuy et al [17] investigate the more symbolic challenge of event anticipation. The information-theoretic measures based on constant and consistent time delays as well as on contingency, that is, proximity in time, are used to automatically detect interesting event dependencies.…”

Section: Wolfram Schenck and Ralf Möllermentioning

confidence: 99%

Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems

Butz¹,

Sigaud²,

Pezzulo³

et al.

Anticipatory Behavior in Adaptive Learning Systems

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Abstract. Research on anticipatory behavior in adaptive learning systems continues to gain more recognition and appreciation in various research disciplines. This book provides an overarching view on anticipatory mechanisms in cognition, learning, and behavior. It connects the knowledge from cognitive psychology, neuroscience, and linguistics with that of artificial intelligence, machine learning, cognitive robotics, and others. This introduction offers an overview over the contributions in this volume highlighting their interconnections and interrelations from an anticipatory behavior perspective. We first clarify the main foci of anticipatory behavior research. Next, we present a taxonomy of how anticipatory mechanisms may be beneficially applied in cognitive systems. With relation to the taxonomy, we then give an overview over the book contributions. The first chapters provide surveys on currently known anticipatory brain mechanisms, anticipatory mechanisms in increasingly complex natural languages, and an intriguing challenge for artificial cognitive systems. Next, conceptualizations of anticipatory processes inspired by cognitive mechanisms are provided. Subsequent chapters address predictive challenges in vision and the processing of event correlations over time. Next, anticipatory mechanisms in individual decision making and behavioral execution are studied. Finally, the book offers systems and conceptualizations of anticipatory processes involved in social interaction.

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Distinction between types of motivations: Emergent behavior with a neural, model-based reinforcement learning system

Shirinov

Butz

2009

2009 IEEE Symposium on Artificial Life

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Construction of an Internal Predictive Model by Event Anticipation

Cited by 3 publications

References 6 publications

Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior

Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior

Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems

Distinction between types of motivations: Emergent behavior with a neural, model-based reinforcement learning system

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