Behavior-Based Systems

  • François MichaudEmail author
  • Monica Nicolescu
Part of the Springer Handbooks book series (SHB)


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 systems and 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.


Mobile Robot Robot Control Robot Interaction Basis Behavior Abstract Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

American Association for Artificial Intelligence


allowing dynamic selection and changes


autonomous robot architecture


broadcast of local eligibility


behavior primitive


Congress on Evolutionary Computation


differential elastic actuator


emotion, motivation and intentional behavior


fusion primitive


hybrid behavior-based architecture


human–robot interaction


in real life


motivated behavioral architecture


move value estimation for robot teams


reinforcement learning


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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