From Reactive to Cognitive Agents: Extending Reinforcement Learning to Generate Symbolic Knowledge Bases

Cerqueira, Romulo; Costa, Alexandre Barreto; McGill, Stephen G.; Lee, Daniel; Pappas, George J.

doi:10.1109/lars.2013.77

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…An example of a cognitive architecture with successful applications in robotics is the Concurrent Autonomous Agent (CAA), an autonomous agent architecture for mobile robots that has already proven to be very powerful [5][6][7]. This agent possesses a three layer architecture, in which each layer is responsible for a different task: the reactive layer runs behaviours with a perception-action cycle; the instinctive layer coordinates reactive behaviour selection; and the cognitive layer does the high-level planning.…”

Section: Introductionmentioning

confidence: 99%

System Level Design and Conception of a System-on-a-Chip (SoC) for Cognitive Robotics

Ferreira¹,

Costa²,

Oliveira³

et al. 2021

Robotics Software Design and Engineering

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In this work, a system level design and conception of a System-on-a-Chip (SoC) for the execution of cognitive agents in robotics will be presented. The cognitive model of the Concurrent Autonomous Agent (CAA), which was already successfully applied in several robotics applications, is used as a reference for the development of the hardware architecture. This cognitive model comprises three levels that run concurrently, namely the reactive level (perception-action cycle that executes predefined behaviours), the instinctive level (receives goals from cognitive level and uses a knowledge based system for selecting behaviours in the reactive level) and the cognitive level (planning). For the development of such system level hardware model, the C++ library SystemC with Transaction Level Modelling (TLM) 2.0 will be used. A system model of a module that executes a knowledge based system is presented, followed by a system level description of a processor dedicated to the execution of the Graphplan planning algorithm. The buses interconnecting these modules are modelled by the TLM generic payload. Results from simulated experiments with complex knowledge bases for solving planning problems in different robotics contexts demonstrate the correctness of the proposed architecture. Finally, a discussion on performance gains takes place in the end.

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Section: Introductionmentioning

confidence: 99%

System Level Design and Conception of a System-on-a-Chip (SoC) for Cognitive Robotics

Ferreira¹,

Costa²,

Oliveira³

et al. 2021

Robotics Software Design and Engineering

View full text Add to dashboard Cite

show abstract

“…An example of architecture for cognitive agents is the concurrent autonomous agent (CAA), an autonomous agent architecture for mobile robots that has already proven to be very powerful [5][6][7]. Three parallel levels compose this architecture: the reactive, the instinctive and the cognitive levels.…”

Section: Introductionmentioning

confidence: 99%

IntelliSoC: A System Level Design and Conception of a System- on-a-Chip (SoC) to Cognitive Agents Architecture

Ferreira¹,

Costa²,

Oliveira³

2019

Applications of Mobile Robots

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This chapter presents a system level design and conception of a System-on-a-Chip (SoC) for the execution of cognitive agents. The computational architecture of this SoC will be presented using the cognitive model of the concurrent autonomous agent (CAA) as a reference. This cognitive model comprises three levels that run concurrently, namely the reactive level, the instinctive level and the cognitive level. The reactive level executes a fast perception-action cycle. The instinctive level receives perceptions from and sends the active behavior to the reactive level, and using a Knowledge Based System (KBS) executes plans by selecting reactive behaviors. The cognitive level receives symbolic information from the instinctive level to update its logical world model, used for planning and sends new local goals to instinctive level. Thus, this work proposes a novel SoC whose architecture fits the computational demands of the aforementioned cognitive model, allowing for fast, energy-efficient, embedded intelligent applications.

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A SystemC Model of a Low-Level Inference Engine for Symbolic Reasoning in Cognitive Agents**This work was supported by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, process number 9174-11-7), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnolgico) and FAPESB (Fundação de Amparo à Pesquisa do Estado da Bahia).

2015

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From Reactive to Cognitive Agents: Extending Reinforcement Learning to Generate Symbolic Knowledge Bases

Cited by 6 publications

References 6 publications

System Level Design and Conception of a System-on-a-Chip (SoC) for Cognitive Robotics

System Level Design and Conception of a System-on-a-Chip (SoC) for Cognitive Robotics

IntelliSoC: A System Level Design and Conception of a System- on-a-Chip (SoC) to Cognitive Agents Architecture

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