An Ontology for Comparative Cognition: A Functional Approach

Franklin, Stan; Ferkin, Michael H.

doi:10.3819/ccbr.2008.10003

Cited by 17 publications

(9 citation statements)

References 90 publications

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“…In addition to providing explanations and producing hypotheses, we aspire that LIDA act as a cognitive prosthesis to aid in thinking about, and understanding, individual cognitive activities and their processes. It should do so by providing a useful cognitive ontology (Franklin & Ferkin, 2006).…”

Section: Introductionmentioning

confidence: 99%

A LIDA cognitive model tutorial

Franklin

Madl

Strain

et al. 2016

Biologically Inspired Cognitive Architectures

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Over a decade in the making and described in some seventy-five published papers, the LIDA cognitive model is comprehensive, complex, and hard to "wrap one's head around". Here we offer, in tutorial fashion, a current, relatively complete and somewhat detailed, description of the conceptual LIDA model, with pointers to more complete accounts of individual processes in the literature. These descriptions also include some features of the workings of the LIDA model that have not been published previously. The tutorial begins with several short sections designed to ease the reader into the LIDA model. These are followed by an account of the conceptual commitments of the LIDA model. We also include a brief introduction to the LIDA computational model via the LIDA Framework, with pointers to its own tutorial. This is followed by sketches of several of the LIDA based agents developed with the help of the Framework. The tutorial ends with a section on current research activity, which includes a table showing which aspects of the LIDA conceptual model have currently been implemented computationally.

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Section: Introductionmentioning

confidence: 99%

A LIDA cognitive model tutorial

Franklin

Madl

Strain

et al. 2016

Biologically Inspired Cognitive Architectures

Self Cite

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“…It can be hypothesized that metacognitive awareness may be one of the keys to developing truly intelligent artificial systems. An autonomous agent is a system situated in, and part of, an environment, which senses that environment, and acts on it, over time, in pursuit of its own agenda [5]. In biological agents, the agenda arises from evolved drives and their associated goals; in artificial agents, the agenda arises from drives and goals built in by their designers.…”

Section: A Metacognitionmentioning

confidence: 99%

Swarm Intelligence to estimate human foreseeability

Swain

Kamila

Misra³

2012

2012 International Conference on Computing, Communication and Applications

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“…Related applications of the SDM can be found in the Learning Intelligent Distribution Agent (LIDA) architecture, a cognitive architecture that comprises modules for perception, various types of memory, "consciousness," action selection, deliberation, and violation [8]. LIDA employs an SDM as its major episodic memory [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Robot navigation and manipulation based on a predictive associative memory

Jockel¹,

Mendes

Zhang³

et al. 2009

2009 IEEE 8th International Conference on Development and Learning

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Abstract-Proposed in the 1980s, the Sparse Distributed Memory (SDM) is a model of an associative memory based on the properties of a high dimensional binary space. This model has received some attention from researchers of different areas and has been improved over time. However, a few problems have to be solved when using it in practice, due to the non-randomness characteristics of the actual data. We tested an SDM using different forms of encoding the information, and in two different domains: robot navigation and manipulation. Our results show that the performance of the SDM in the two domains is affected by the way the information is actually encoded, and may be improved by some small changes in the model.

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An Ontology for Comparative Cognition: A Functional Approach

Cited by 17 publications

References 90 publications

A LIDA cognitive model tutorial

A LIDA cognitive model tutorial

Swarm Intelligence to estimate human foreseeability

Robot navigation and manipulation based on a predictive associative memory

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