The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments. In this paper we introduce the concepts of online and offline sampling and highlight the role of online sampling in agent-based models. After comparing the strengths of each approach for modeling particular developmental phenomena and research questions, we describe a recent agent-based model of infant causal perception. We conclude by discussing some of the present limitations of agent-based models and suggesting how these challenges may be addressed. © 2001 Academic Press Computational models of learning and development are playing an increasingly critical role in child development research (Cassidy, 1990;

Soft constraints hypothesis (SCH) is a rational analysis approach that holds that the mixture of perceptual-motor and cognitive resources allocated for interactive behavior is adjusted based on temporal cost-benefit tradeoffs. Alternative approaches maintain that cognitive resources are in some sense protected or conserved in that greater amounts of perceptual-motor effort will be expended to conserve lesser amounts of cognitive effort. One alternative, the minimum memory hypothesis (MMH), holds that people favor strategies that minimize the use of memory. SCH is compared with MMH across 3 experiments and with predictions of an Ideal Performer Model that uses ACT-R’s memory system in a reinforcement learning approach that maximizes expected utility by minimizing time. Model and data support the SCH view of resource allocation; at the under 1000-ms level of analysis, mixtures of cognitive and perceptual-motor resources are adjusted based on their cost-benefit tradeoffs for interactive behavior.

Trial-and-error learning strategies play a central role in sensorimotor development during early infancy. However, learning to reach by trial-and-error normally requires a slow and laborious search through the space of possible movements. We propose a computational model of reaching based on the notion that early sensorimotor control is driven by the generation of exploratory movements, followed by the selection and maintenance of adaptive movement patterns. We find that instead of exhaustively exploring the full search space of movement patterns, the model exploits several emergent constraints that limit the initial size of the movement search space. These constraints exploit both mechanical and kinematic properties of the reaching task. We relate these results to the development of reaching during infancy, and discuss recent findings that have identified similar constraints in young infants. Learning to Reach 3 Learning to Reach by Constraining the Movement Search Space Several rec...

Minimum (or minimal) principles are mathematical laws that were first used in physics: Hamilton's principle and Fermat's principle of least time are two famous example. In the past decade, a number of motor control theories have been proposed that are formally of the same kind as the minimum principles of physics, and some of these have been quite successful at predicting motor performance in a variety of tasks. The present paper provides a comprehensive review of this work. Particular attention is given to the relation between minimum theories in motor control and those used in other disciplines. Other issues around which the review is organized include: (1) the relation between minimum principles and structural models of motor planning and motor control, (2) the empirically-driven development of minimum principles and the danger of circular theorizing, and (3) the design of critical tests for minimum theories. Some perspectives for future research are discussed in the concluding section of the paper.

Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of responding. The model is consistent with what is known about how neural systems evaluate behavior. The authors model the development of reaching and investigate N. Bernstein’s (1967) hypotheses about early motor learning. Simulations show the course of learning as well as model the kinematics of reaching by a dynamical arm. Almost all developmental theories assume that a child’s interaction with the environment plays an important role in development. Often this interaction leads to long-term changes in behavior that can best be described as learning. Modern theories of learning characterize the process as exploratory, as involving the variation and selection of behavior or strategies, or as the discovery of

Within a collaboration between computer scientists and psychologists, we are studying the acquisition and development of behaviors by animals, including human beings. The central hypothesis is that the behavior follows Thondike's law of effect (Thorndike, 1911) which indicates that the probability of emission of a behavior that is followed by favorable consequences increases. This law has largely been studied experimentally since then to explain and predict animal behavior. It is also known as the selection of behaviors by their consequences. We have implemented this law using reinforcement algorithms and we have been able to reproduce experiments involving human beings via simulations of reinforcement agents. Using this kind of techniques, we have recently been able to simulate the acquisition of an arm reaching movement. The simulation shows a remarkable similarity with the behavior of the human baby. It is able to reach different positions in the space and maintain these different movements. The movements, initially uncoordinated, become smooth and reach directly the target. In a close future, we will implement these methods in hardware robots.

This study presents a computational model for learning to generate complex extended actions from examples. The continuous trajectory is represented in terms of elementary actions. The sequence of elementary actions move the plant through intermediate states that form compact clusters in state space. The compact clusters allow efficient interpolation and generation of new trajectories. 1 Introduction Learning from practice is one of the basic functional abilities of the human brain. Considerable progress has been made in the study and modeling of this capacity. A particularly challenging problem in this area is the learning of complex extended activity, such as the control of complex arm movements from example movements. In this paper we describe a model that learns to generate extended actions. The model first generates basic actions between given initial and final states. This initial phase of learning requires a considerable amount of time and computing resources. It results, ...

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Abstract--By the onset of reaching, young infants are already able to keep track of the position of their han by using visual feedback from the target and proprioceptive feedback from the arm. How is this multimodal coordination achieved? We propose that infants learn to coordinate vision and proprioception by using tactile feedback from the target. In order to evaluate this hypothesis, we employ an evolutionarybased learning algorithm as a proxy for trial-and-error sensorimotor development in young infants. A series of simulation studies illustrate how touch (1) help coordinate vision and proprioception, (2) facilitates a efficient reaching strategy, and (3) promotes intermodal recalibration when the coordination is perturbed. We present two developmental predictions generated by the model, and discuss the relative importance of visual and tactile feedback while learning to reach. Index terms--computational model, sensorimotor development, trial-and-error learning, multimodal coordination I.

Trial-and-error learning strategies play a central role in sensorimotor development during early infancy. However, learning to reach by trial-and-error normally requires a slow and laborious search through the space of possible movements. We propose a computational model of reaching based on the notion that early sensorimotor control is driven by the generation of exploratory movements, followed by the selection and maintenance of adaptive movement patterns. We find that instead of exhaustively exploring the full search space of movement patterns, the model exploits several emergent constraints that limit the initial size of the movement search space. These constraints exploit both mechanical and kinematic properties of the reaching task. We relate these results to the development of reaching during infancy, and discuss recent findings that have identified similar constraints in young infants. Learning to Reach 3 Learning to Reach by Constraining the Movement Search Space Several rec...