Behavior-Based Systems

Abstract

Nature is filled with examples of autonomous creatures capable of dealing with the diversity, unpredictability, and rapidly changing conditions of the real world. Such creatures must make decisions and take actions based on incomplete perception, time constraints, limited knowledge about the world, cognition, reasoning and physical capabilities, in uncontrolled conditions and with very limited cues about the intent of others. Consequently, one way of evaluating intelligence is based on the creature’s ability to make the most of what it has available to handle the complexities of the real world. The main objective of this chapter is to explain behavior-based systemsand their use in autonomous control problems and applications. The chapter is organized as follows. Section 13.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 13.2 follows by outlining the basic principles of behavior-based systems that make them distinct from other types of robot control architectures. The concept of basis behaviors, the means of modularizing behavior-based systems, is presented in Sect. 13.3. Section 13.4 describes how behaviors are used as building blocks for creating representations for use by behavior-based systems, enabling the robot to reason about the world and about itself in that world. Section 13.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multi-robot systems. Section 13.6 provides an overview of various robotics problems and application domains that have successfully been addressed or are currently being studied with behavior-based control. Finally, Sect. 13.7 concludes the chapter.

AAAI

American Association for Artificial Intelligence

APOC

allowing dynamic selection and changes

AuRA

autonomous robot architecture

BLE

broadcast of local eligibility

BP

behavior primitive

CEC

Congress on Evolutionary Computation

DEA

differential elastic actuator

EMIB

emotion, motivation and intentional behavior

FP

fusion primitive

HBBA

hybrid behavior-based architecture

HRI

human–robot interaction

IRL

in real life

MBA

motivated behavioral architecture

MVERT

move value estimation for robot teams

RL

reinforcement learning

SLAM

simultaneous localization and mapping

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer EngineeringUniversity of SherbrookeSherbrookeCanada
  2. 2.Department of Computer Science and EngineeringUniversity of NevadaRenoUSA

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