Titlebook: Computational and Robotic Models of the Hierarchical Organization of Behavior; Gianluca Baldassarre,Marco Mirolli Book 2013 Springer-Verla

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Adornment

Behavioral Hierarchy: Exploration and Representation ranges of behavior. Hierarchies of behavioral modules facilitate learning complex skills and planning at multiple levels of abstraction and enable agents to incrementally improve their competence for facing new challenges that arise over extended periods of time. This chapter focuses on two feature

大罵

Self-Organized Functional Hierarchy Through Multiple Timescales: Neuro-dynamical Accounts for Behavies. The models have been examined through robot experiments for the purpose of exploring novel phenomena appearing in the interaction between neural dynamics and physical actions, which could provide us new insights to understand nontrivial brain mechanisms. Those robot experiments successfully show

廢墟

Autonomous Representation Learning in a Developing Agentons through interaction with the environment. This chapter focuses on the problem of learning representations. We present four principles for autonomous learning of representations in a developing agent, and we demonstrate how these principles can be embodied in an algorithm. In a simulated environm

disciplined

Hierarchies for Embodied Action Perceptionge, and physical, perceptual and computational constraints. This capability relies on action perception mechanisms that exploit regularities in observed goal-oriented behaviours to generate robust predictions and reduce the workload of sensing systems. To achieve this essential capability, we argue

斗爭(zhēng)

Learning and Coordinating Repertoires of Behaviors with Common Reward: Credit Assignment and Module ing multiple concurrent goals such as foraging for different foods while avoiding different predators and looking for a mate. A promising way to do so is reinforcement learning (RL) as it considers in a very general way the problem of choosing actions in order to maximize a measure of cumulative ben

斗爭(zhēng)

兇殘

Generalization and Interference in Human Motor Controlace two fundamental issues: (1) they must acquire new skills in a ., that is exploiting previous knowledge to learn new behaviors, and (2) they must avoid the so-called ., where learning new knowledge destroys existing memories. Here, we analyze the problem from the perspective of biological motor c

disciplined

A Developmental Framework for Cumulative Learning Robotsbehaviour and our ability to implement developmental processes in autonomous agents. In this chapter we describe an approach towards developmental growth for robotics that utilises natural constraints in a general learning mechanism. The method, summarised as Lift-Constraint, Act, Saturate (LCAS), i

faddish

The Hierarchical Accumulation of Knowledge in the Distributed Adaptive Control Architecture converted into information that is converted into knowledge. Moreover, theories on cumulative learning suggest that different cognitive layers accumulate this knowledge, building highly complex skills from low complexity ones. The biologically based Distributed Adaptive Control cognitive architectu