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73
Toward the Evolution of Dynamical Neural Networks for Minimally Cognitive Behavior
, 1996
"... Current debates regarding the possible cognitive implications of ideas from adaptive behavior research and dynamical systems theory would benefit greatly from a careful study of simple model agents that exhibit minimally cognitive behavior. This paper sketches one such agent, and presents the result ..."
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Cited by 99 (9 self)
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Current debates regarding the possible cognitive implications of ideas from adaptive behavior research and dynamical systems theory would benefit greatly from a careful study of simple model agents that exhibit minimally cognitive behavior. This paper sketches one such agent, and presents the results of preliminary experiments on the evolution of dynamical neural networks for visually-guided orientation, object discrimination and accurate pointing with a simple manipulator to objects appearing in its field of view. 1 Introduction Many of the key ideas emphasized in adaptive behavior research are beginning to have a significant impact on cognitive science. For example, adaptive behavior research in general, and the dynamical perspective on adaptive behavior that is often taken in such research in particular, have begun to significantly influence the growing debates concerning the nature and necessity of notions of representation and computation in explaining cognitive behavio...
The dynamics of active categorical perception in an evolved model agent
- ADAPTIVE BEHAVIOR
, 2003
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Intelligence by Design: Principles of Modularity and Coordination for Engineering Complex Adaptive Agents
, 2001
"... All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This d ..."
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Cited by 62 (21 self)
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All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation
Six views of embodied cognition
- PSYCHONOMIC BULLETIN AND REVIEW
, 2002
"... The emerging viewpoint of embodied cognition holds that cognitive processes are deeply rooted in the body’s interactions with the world. This position actually houses a number of distinct claims, some of which are more controversial than others. This paper distinguishes and evaluates the following s ..."
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Cited by 60 (0 self)
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The emerging viewpoint of embodied cognition holds that cognitive processes are deeply rooted in the body’s interactions with the world. This position actually houses a number of distinct claims, some of which are more controversial than others. This paper distinguishes and evaluates the following six claims: 1) cognition is situated; 2) cognition is time-pressured; 3) we off-load cognitive work onto the environment; 4) the environment is part of the cognitive system; 5) cognition is for action; 6) off-line cognition is body-based. Of these, the first three and the fifth appear to be at least partially true, and their usefulness is best evaluated in terms of the range of their applicability. The fourth claim, I argue, is deeply problematic. The sixth claim has received the least attention in the literature on embodied cognition, but it may in fact be the best documented and most powerful of the six claims.
Go to the ant: engineering principles from natural multi agent systems. Annls Ops Res
, 1997
"... Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing ..."
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Cited by 43 (1 self)
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Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents. 1.
Towards a cognitive robotics
- Adaptive Behavior
, 1999
"... Additional services and information for Adaptive Behavior can be found at: ..."
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Cited by 33 (2 self)
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Additional services and information for Adaptive Behavior can be found at:
Rethinking Grounding
- In
, 1999
"... The ‘grounding problem ’ poses the question of how the function and internal mechanisms of a machine, natural or artificial, can be intrinsic to the machine itself, i.e. independent of an external designer or observer. Searle’s and Harnad’s analyses of the grounding problem are briefly reviewed as w ..."
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Cited by 29 (10 self)
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The ‘grounding problem ’ poses the question of how the function and internal mechanisms of a machine, natural or artificial, can be intrinsic to the machine itself, i.e. independent of an external designer or observer. Searle’s and Harnad’s analyses of the grounding problem are briefly reviewed as well as different approaches to solving it, based on the cognitivist and the enactive paradigms in cognitive science. It is argued that, although the two categories of grounding approaches differ in their nature and the problems they have to face, both, so far, fail to provide fully grounded systems for similar reasons: Only isolated parts of systems are grounded, whereas other, essential, parts are left ungrounded. Hence, it is further argued that grounding should instead be understood and approached as radical bottom-up development of complete robotic agents in interaction with their environment.
Challenging the Computational Metaphor: Implications for How We Think
, 1999
"... This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor --- a sequence of steps --- with the notio ..."
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Cited by 27 (2 self)
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This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor --- a sequence of steps --- with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think.
Neural blackboard architectures of combinatorial structures in cognition
- Behavioral and Brain Sciences
, 2006
"... Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore e ..."
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Cited by 22 (1 self)
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Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff formulated four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard ’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. 2 Content
An Interactivist-Constructivist Approach to Intelligence: Self-Directed Anticipative Learning
- In The Collected Works of John Dewey
, 2000
"... Abstract This paper outlines an original interactivist-constructivist (I-C) approach to modeling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realised in the brain. Two key ideas for concep ..."
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Cited by 20 (2 self)
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Abstract This paper outlines an original interactivist-constructivist (I-C) approach to modeling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realised in the brain. Two key ideas for conceptualising intelligence within this framework are developed. These are: (i) intelligence is centrally concerned with the capacity for coherent, contextsensitive, self-directed management of interaction, (ii) the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process modulation which allows a system to ‘steer ’ itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified (e.g. genetically) learning is an important component of intelligence. A model of self-directed anticipative learning (SDAL) is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined, problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modeling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski’s recent neural constructivism (NC) paradigm, enriched by Montague and Sejnowski’s dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a four-fold reflection on the divergence in cognitive modeling philosophy between the I-C and the traditional computational information processing (CIP) approaches. 1.

