A framework for plan execution in behavior-based robots

Hertzberg, Joachim; Jaeger, Herbert; Zimmer, Uwe R.; Morignot, Philippe

doi:10.1109/isic.1998.713627

Cited by 15 publications

(12 citation statements)

References 11 publications

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“…The dual dynamics framework (Hertzberg et al, 1998;Jaeger and Christaller, 1998) represents activation dynamics as different from target dynamics, analogous to intentions and navigation dynamics in DI-HDCAs. Dynamic neural field approaches (Schöner et al, 1995;Erlhagen and Bicho, 2006;Richter et al, 2012;Sandamirskaya et al, 2013), based on neuroscientific principles, also associate activations of cognitive entities with actuations of behaviors.…”

Section: Cognitive Modeling and Goal-directed Reasoningmentioning

confidence: 99%

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Aaron

2016

Front. Robot. AI

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Intelligent embodied robots are integrated systems: as they move continuously through their environments, executing behaviors and carrying out tasks, components for low-level and high-level intelligence are integrated in the robot's cognitive system, and cognitive and physical processes combine to create their behavior. For a modeling framework to enable the design and analysis of such integrated intelligence, the underlying representations in the design of the robot should be dynamically sensitive, capable of reflecting both continuous motion and micro-cognitive influences, while also directly representing the necessary beliefs and intentions for goal-directed behavior. In this paper, a dynamical intention-based modeling framework is presented that satisfies these criteria, along with a hybrid dynamical cognitive agent (HDCA) framework for employing dynamical intentions in embodied agents. This dynamical intention-HDCA (DI-HDCA) modeling framework is a fusion of concepts from spreading activation networks, hybrid dynamical system models, and the BDI (belief-desire-intention) theory of goal-directed reasoning, adapted and employed unconventionally to meet entailments of environment and embodiment. The paper presents two kinds of autonomous agent learning results that demonstrate dynamical intentions and the multi-faceted integration they enable in embodied robots: with a simulated service robot in a grid-world office environment, reactive-level learning minimizes reliance on deliberative-level intelligence, enabling task sequencing and action selection to be distributed over both deliberative and reactive levels; and with a simulated game of Tag, the cognitive-physical integration of an autonomous agent enables the straightforward learning of a user-specified strategy during gameplay, without interruption to the game. In addition, the paper argues that dynamical intentions are consistent with cognitive theory underlying goal-directed behavior, and that DI-HDCA modeling may facilitate the study of emergent behaviors in embodied agents.

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Section: Cognitive Modeling and Goal-directed Reasoningmentioning

confidence: 99%

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Aaron

2016

Front. Robot. AI

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“…For example, the dual dynamics framework [11,12] explicitly represents activation dynamics distinct from target dynamics, which are analogous to HDCAs' dynamics of intention evolution and navigation dynamics, respectively. Dynamic neural field approaches [13,14] also associate activations of cognitive entities with actuations of behaviors in a way that can be readily seen as representing intention.…”

Section: Bdi and Reactive Intentionmentioning

confidence: 99%

Action selection and task sequence learning for hybrid dynamical cognitive agents

Aaron

Admoni

2010

Robotics and Autonomous Systems

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“…Work that is not directly focused on fault detection and isolation, but still related to execution monitoring is the work on the Dual Dynamics & Planning (DD&P) robot control architecture, see [35,72]. This work is related to execution monitoring since the DD&P architecture has an interesting technique for extracting information about the robot and its performance.…”

Section: Expert Systemsmentioning

confidence: 99%

Execution monitoring in robotics: A survey

Pettersson

2005

Robotics and Autonomous Systems

148

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Research on execution monitoring in its own is still not very common within the field of robotics and autonomous systems. It is more common that researchers interested in control architectures or execution planning include monitoring as a small part of their work when they realize that it is needed. On the other hand, execution monitoring has been a well studied topic within industrial control, although control theorists seldom use this term. Instead they refer to the problem of fault detection and isolation (FDI). This survey will use the knowledge and terminology from industrial control in order to classify different execution monitoring approaches applied to robotics. The survey is particularly focused on autonomous mobile robotics.

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A framework for plan execution in behavior-based robots

Cited by 15 publications

References 11 publications

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Action selection and task sequence learning for hybrid dynamical cognitive agents

Execution monitoring in robotics: A survey

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