Extracting Situation Facts from Activation Value Histories in Behavior-Based Robots

Schönherr, Frank; Cistelecan, Mihaela R.; Hertzberg, Joachim; Christaller, Thomas

doi:10.1007/3-540-45422-5_22

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…This particular information, that is, symbolic ground facts, that are given to the world-model, are obtained by analyzing the activation levels of the behaviors. Four specific features are extracted from the activations levels over time [71]: rising edge, falling edge, high level, and low level. The key idea in extracting facts from activation levels is to consider patterns of qualitative activations of several behaviors that occur within the same interval of time.…”

Section: Expert Systemsmentioning

confidence: 99%

Execution monitoring in robotics: A survey

Pettersson

2005

Robotics and Autonomous Systems

146

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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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Section: Expert Systemsmentioning

confidence: 99%

Execution monitoring in robotics: A survey

Pettersson

2005

Robotics and Autonomous Systems

146

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“…Our view of building hybrid robot controllers involving a behavior-based robot control system (BBS) [Mat99] as reactive component is shaped by our work in progress on the dd&p robot control architecture [HJZM98,HS01,SCHC01]. The demo example we will present is formulated in a concrete BBS framework, namely, Dual Dynamics (DD, [JC97]) and we will illustrate how to blend it with classical propositional action planners.…”

Section: Background and Overviewmentioning

confidence: 99%

“…A matching front in c vanishes if it reaches the end point t (the defining chronicle is true), or else while stuck at t i is caught up by another matching front at t i , or else an activation gestalt over an interval ending at t i+1 is no longer valid in the current qualitative activation history. The complexity of this process is O(|Behaviors|*|Chronicles|*max|AGsInChronicle|), see [SCHC01] for details.…”

Section: From (Re-)action To Symbols: Extracting Facts From Activatiomentioning

confidence: 99%

“…On appearance of some trigger condition in the qualitative activation log, we try to match the activation gestalts prior to the trigger with qualitative activations in the log file, and, if successful, verify the gestalts after the trigger condition in the qualitative activations as they are being logged. [SCHC01] gives an example where the derivation of the InRoom fact fails and a trigger condition would save unnecessary matching effort.…”

Section: From (Re-)action To Symbols: Extracting Facts From Activatiomentioning

confidence: 99%

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Advances in Plan-Based Control of Robotic Agents

Beetz¹,

Hertzberg²,

Ghallab³

et al. 2002

Lecture Notes in Computer Science

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PrefaceIn recent years, autonomous robots, including Xavier, Martha [1], Rhino [2,3], Minerva, and Remote Agent, have shown impressive performance in long-term demonstrations. In NASA's Deep Space program, for example, an autonomous spacecraft controller, called the Remote Agent [5], has autonomously performed a scientific experiment in space. At Carnegie Mellon University, Xavier [6], another autonomous mobile robot, navigated through an office environment for more than a year, allowing people to issue navigation commands and monitor their execution via the Internet. In 1998, Minerva [7] acted for 13 days as a museum tourguide in the Smithsonian Museum, and led several thousand people through an exhibition.These autonomous robots have in common that they rely on plan-based control in order to achieve better problem-solving competence. In the plan-based approach, robots generate control actions by maintaining and executing a plan that is effective and has a high expected utility with respect to the robots' current goals and beliefs. Plans are robot control programs that a robot can not only execute but also reason about and manipulate [4]. Thus, a plan-based controller is able to manage and adapt the robot's intended course of action -the plan -while executing it and can thereby better achieve complex and changing tasks. The plans used for autonomous robot control are often reactive plans, that is, they specify how the robots are to respond in terms of low-level control actions to continually arriving sensory data in order to accomplish their objectives. The use of plans enables these robots to flexibly interleave complex and interacting tasks, exploit opportunities, quickly plan their courses of action, and, if necessary, revise their intended activities.The emergence of complete plan-based robot control systems, on the one hand, and the lack of integrated computational models of plan-based control, on the other hand, motivated us to organize a Dagstuhl seminar "Plan-Based Control of Robotic Agents" 1 at Schloss Dagstuhl (21-26 October 2001). The purpose of this seminar was to bring together researchers from different fields in order to promote information exchange and interaction between research groups working on various aspects of plan-based autonomous robot control.The objective was to identify computational principles that enable autonomous robots to accomplish complex, diverse, and dynamically changing tasks in challenging environments. These principles include plan-based high-level control, probabilistic reasoning, plan transformation, and context and resourceadaptive reasoning. The development of comprehensive and integrated computational models of plan-based control requires us to consider different aspects of plan-based control -plan representation, reasoning, execution, and learningtogether and not in isolation. Such integrated approaches enable us to exploit synergies between the different aspects and thereby come up with simpler and more powerful computational models.1 For more information on the Se...

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“…It is not only the instantaneous sensory inputs that are needed to solve the tasks, but also the sensory history [12] and dynamics.…”

Section: Introductionmentioning

confidence: 99%

Event Detection and Localization in Mobile Robot Navigation Using Reservoir Computing

Antonelo

Schrauwen

Dutoit

et al. 2007

Lecture Notes in Computer Science

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Abstract. Reservoir Computing (RC) uses a randomly created recurrent neural network where only a linear readout layer is trained. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization based solely on sensory information. The robot thus builds an implicit map of the environment without the use of odometry data. These techniques are demonstrated in simulation on several complex and even dynamic environments.

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Extracting Situation Facts from Activation Value Histories in Behavior-Based Robots

Cited by 7 publications

References 8 publications

Execution monitoring in robotics: A survey

Execution monitoring in robotics: A survey

Advances in Plan-Based Control of Robotic Agents

Event Detection and Localization in Mobile Robot Navigation Using Reservoir Computing

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