Architecture and Representations

Hawes, Nick; Wyatt, Jeremy; Sridharan, Mohan; Jacobsson, Henrik; Dearden, Richard; Sloman, Aaron; Kruijff, Geert-Jan M.

doi:10.1007/978-3-642-11694-0_2

Cited by 8 publications

(9 citation statements)

References 16 publications

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“…The Dora system described in this paper comprises more than 100 concurrently running components. We refer the reader to more detailed descriptions of our architectural approach elsewhere, as while important, it is not the focus of this paper [16,19,17]. This shows the plan generated for the goal "find a magazine" from the initial state.…”

Section: A Three-level Architecturementioning

confidence: 99%

“…The number of virtual objects can be scaled as required for a domain. 17 This use of assumptive actions with default knowledge provides our solution to problem (ii): open-world planning.…”

Section: Instance Assumptions Based On Default Knowledgementioning

confidence: 99%

“…In our running example, the following assumptions allow the planner to assume the existence of a meeting room: 17 For example, domains with many unknown rooms and objects would require many virtual objects. (:assumption leads-to-room-placeholder1-meetingroom :effect (and (A (leads-to-room placeholder1 meetingroom) true) (assign (probability) 0.304))) (:assumption room-from-placeholder :parameters (?p -place ?r -room ?c -category) :precondition (and (= (placestatus ?p) placeholder) (A (leads-to-room ?p ?c) true) (is-virtual ?r)) :effect (and (assign (in-room ?p) ?r) (assign (category ?r) ?c)))…”

Section: Planning In Open Worlds: An Examplementioning

confidence: 99%

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Robot task planning and explanation in open and uncertain worlds

Hanheide¹,

Gbelbecker²,

Horn³

et al. 2017

Artificial Intelligence

Self Cite

113

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A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization.

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Section: A Three-level Architecturementioning

confidence: 99%

“…The number of virtual objects can be scaled as required for a domain. 17 This use of assumptive actions with default knowledge provides our solution to problem (ii): open-world planning.…”

Section: Instance Assumptions Based On Default Knowledgementioning

confidence: 99%

Section: Planning In Open Worlds: An Examplementioning

confidence: 99%

See 1 more Smart Citation

Robot task planning and explanation in open and uncertain worlds

Hanheide¹,

Gbelbecker²,

Horn³

et al. 2017

Artificial Intelligence

Self Cite

113

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“…Erman, Hayes-Roth, Lesser, and Reddy (1980)). Our architectural approach is described in more detail in existing work ; Hawes, Wyatt, Sridharan, Jacobsson, et al (2010)). As depicted in Figure 5, George is composed of six CAST SAs.…”

Section: Integrated Systemmentioning

confidence: 99%

An integrated system for interactive continuous learning of categorical knowledge

Skočaj

Vrečko

Mahnič

et al. 2016

Journal of Experimental & Theoretical Artificial Intelligen

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This article presents an integrated robot system capable of interactive learning in dialogue with a human. Such a system needs to have several competencies and must be able to process different types of representations. In this article, we describe a collection of mechanisms that enable integration of heterogeneous competencies in a principled way. Central to our design is the creation of beliefs from visual and linguistic information, and the use of these beliefs for planning system behaviour to satisfy internal drives. The system is able to detect gaps in its knowledge and to plan and execute actions that provide information needed to fill these gaps. We propose a hierarchy of mechanisms which are capable of engaging in different kinds of learning interactions, e.g. those initiated by a tutor or by the system itself. We present the theory these mechanisms are build upon and an instantiation of this theory in the form of an integrated robot system. We demonstrate the operation of the system in the case of learning conceptual models of objects and their visual properties

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“…A specific requirement for our application is real-time performance under massive data processing over streaming audio and video; this ruled out the existing multimodal dialogue platforms, and also led us to eschew standards-based APIs (e.g., as used in [5]) which incur overheads on message-passing to components. The CoSy Architecture [6] shares similar motivations and characteristics. Fig.…”

Section: Integration Frameworkmentioning

confidence: 99%

Thinking head: Towards human centred robotics

Herath¹,

Kroos²,

Stevens³

et al. 2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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Abstract-Thinking Head project is a multidisciplinary approach to building intelligent agents for human machine interaction. The Thinking Head Framework evolved out of the Thinking Head Project and it facilitates loose coupling between various components and forms the central nerve system in a multimodal perception-action system. The paper presents the overall architecture, components and the attention system. The paper then concludes with a preliminary behavioral experiment that studies the intelligibility of the audiovisual speech output produced by the Embodied Conversational Agent (ECA) that is part of the system. These results provide the baseline for future evaluations of the system as the project progresses through multiple evaluate and refine cycles.

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Architecture and Representations

Cited by 8 publications

References 16 publications

Robot task planning and explanation in open and uncertain worlds

Robot task planning and explanation in open and uncertain worlds

An integrated system for interactive continuous learning of categorical knowledge

Thinking head: Towards human centred robotics

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