Integration of Advice in an Action-Selection Architecture

Carpenter, Paul; Riley, Patrick; Veloso, Manuela; Kaminka, Gal A.

doi:10.1007/978-3-540-45135-8_15

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Carpenter et al [9] developed an architecture for robot soccer in which deliberate advice from a coach is integrated via the addition of suggested behaviors to the set of executable behaviors. The effect of this advice, however, is highly dependent on the scoring function used by the behavioral arbiter and appears unable to be influence behavioral selection in a non-binary manner.…”

Section: Hybrid Reactive-deliberative Architecturesmentioning

confidence: 99%

Biasing behavioral activation with intent for an entertainment robot

Ulam

Arkin

2008

Intel Serv Robotics

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Deliberate control of an entertainment robot presents a special problem in balancing the requirement for intentional behavior with the existing mechanisms for autonomous action selection. It is proposed that the intentional biasing of activation in lower-level reactive behaviors is the proper mechanism for realizing such deliberative action. In addition, it is suggested that directed intentional bias can result in goal-oriented behavior without subsuming the underlying action selection used to generate natural behavior. This objective is realized through a structure called the intentional bus. The intentional bus serves as the interface between deliberative and reactive control by realizing highlevel goals through the modulation of intentional signals sent to the reactive layer. A deliberative architecture that uses the intentional bus to realize planned behavior is described. In addition, it is shown how the intentional bus framework can be expanded to support the serialization of planned behavior by shifting from direct intentional influence for plan execution to attentional triggering of a learned action sequence. Finally, an implementation of this architecture, developed and tested on Sony's humanoid robot QRIO, is described.

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Section: Hybrid Reactive-deliberative Architecturesmentioning

confidence: 99%

Biasing behavioral activation with intent for an entertainment robot

Ulam

Arkin

2008

Intel Serv Robotics

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“…The abstract nature and accessibility of declarative knowledge enables us to express it in natural language, and encode it in the robot's controller. It also enables humans to give advice to robots in a declarative way [13].…”

Section: Declarative Knowledgementioning

confidence: 99%

Combining declarative, procedural, and predictive knowledge to generate, execute, and optimize robot plans

Stulp

Beetz

2008

Robotics and Autonomous Systems

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Phase transition and heuristic search in relational learning

Cetina

2007

Sixth International Conference on Machine Learning and Applications (ICMLA 2007)

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Several works have shown that the covering test in relational learning exhibits a phase transition in its covering probability. It is argued that this phase transition dooms every learning algorithm to fail to identify a target concept lying close to it. However, in this paper we exhibit a counter-example which shows that this conclusion must be qualified in the general case. Mostly building on the work of Winston on near-misse examples, we show that, on the same set of problems, a top-down data-driven strategy can cross any plateau if near-misses are supplied in the training set, whereas they do not change the plateau profile and do not guide a generate-and-test strategy. We conclude that the location of the target concept with respect to the phase transition alone is not a reliable indication of the learning problem difficulty as previously thought.

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Integration of Advice in an Action-Selection Architecture

Cited by 4 publications

References 12 publications

Biasing behavioral activation with intent for an entertainment robot

Biasing behavioral activation with intent for an entertainment robot

Combining declarative, procedural, and predictive knowledge to generate, execute, and optimize robot plans

Phase transition and heuristic search in relational learning

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