The Glair Cognitive Architecture

Shapiro, Stewart; Bona, Jonathan P.

doi:10.1142/s1793843010000515

Cited by 27 publications

(24 citation statements)

References 34 publications

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“…For instances, GLAIR is able to "represent and reason about beliefs about itself " (Shapiro and Bona, 2010). Sigma has the function of "architectural selfmonitoring" (Rosenbloom et al, 2016).…”

Section: The Need For a Selfmentioning

confidence: 99%

Self in NARS, an AGI System

Wang

Hammer

2018

Front. Robot. AI

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This article describes and discusses the self-related mechanisms of a general-purpose intelligent system, NARS. This system is designed to be adaptive and to work with insufficient knowledge and resources. The system's various cognitive functions are uniformly carried out by a central reasoning-learning process following a "non-axiomatic" logic. This logic captures the regularities of human empirical reasoning, where all beliefs are revisable according to evidence, and the meaning of concepts are grounded in the system's experience. NARS perceives its internal environment basically in the same way as how it perceives its external environment although the sensors involved are completely different. Consequently, its self-knowledge is mostly acquired and constructive, while being incomplete and subjective. Similarly, self-control in NARS is realized using mental operations, which supplement and adjust the automatic inference control routine. It is argued that a general-purpose intelligent system needs the notion of a "self," and the related knowledge and functions are developed gradually according to the system's experience. Such a mechanism has been implemented in NARS in a preliminary form.

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Section: The Need For a Selfmentioning

confidence: 99%

Self in NARS, an AGI System

Wang

Hammer

2018

Front. Robot. AI

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“…Such systems should be capable of -among others -interpreting and re-interpreting signs, learning, belief revision in the context of new experiences, etc. SNePS/GLAIR agents capable of using natural language utterances and representing knowledge in the form of semantic networks meet these requirements (Shapiro & Bona, 2010;Shapiro & Rapaport, 1987). 1…”

Section: The Problemmentioning

confidence: 99%

“…GLAIR (Grounded Layered Architecture with Integrated Reasoning) with SNePS (Semantic Network Processing System) (Shapiro & Bona, 2010) as a knowledge representation formalism is a multi-layered cognitive architecture for embodied agents acting in real, virtual, or simulated environments containing other agents. The architecture provides 3 layers (cf.…”

Section: Artificial Cognitive Agentmentioning

confidence: 99%

On a Cognitive Model of Semiosis

Konderak

2015

Studies in Logic, Grammar and Rhetoric

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Abstract. What is the class of possible semiotic systems? What kinds of systems could count as such systems? The human mind is naturally considered the prototypical semiotic system. During years of research in semiotics the class has been broadened to include i.e. living systems (Zlatev, 2002) like animals, or even plants (Krampen, 1992). It is suggested in the literature on artificial intelligence that artificial agents are typical examples of symbol-processing entities. It also seems that (at least some) semiotic processes are in fact cognitive processes. In consequence, it is natural to ask the question about the relation between semiotic studies and research on artificial cognitive systems within cognitive science. Consequently, my main question concerns the problem of inclusion or exclusion from the semiotic spectrum at least some artificial (computational) systems. I would like to consider some arguments against the possibility of artificial semiotic systems and I will try to repeal them. Then I will present an existing natural-language using agent of the SNePS system and interpret it in terms of Peircean theory of signs. I would like also to show that some properties of semiotic systems in Peircean sense could be also found in a discussed artificial system. Finally, I will have some remarks on the status of semiotics in general.

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“…In the current literature in robotics and cognitive architectures, the term context usually simply refers to a set of beliefs that initiate a representation (and reasoning) frame: in [76], the robot creates a context of interaction with a specific human by storing in a separate model the beliefs of this human and using this knowledge when dialoging with the human, the reasoning network is reinitialised in the GLAIR architecture when the hypotheses that defined the current situation are not believed anymore [118].…”

Section: Goalmentioning

confidence: 99%

Grounding the Interaction: Knowledge Management for Interactive Robots

Lemaignan

2013

Künstl Intell

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The Glair Cognitive Architecture

Cited by 27 publications

References 34 publications

Self in NARS, an AGI System

Self in NARS, an AGI System

On a Cognitive Model of Semiosis

Grounding the Interaction: Knowledge Management for Interactive Robots

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