A hybrid agent architecture integrating desire, intention and reinforcement learning

A hybrid agent architecture integrating desire, intention and reinforcement learning

This paper presents a hybrid agent architecture that integrates the behaviours of BDI agents, specifically desire and intention, with a neural network based reinforcement learner known as Temporal DifferenceFusion Architecture for Learning and COgNition (TD-FALCON). With the explicit maintenance of...

Full description

Saved in:

Bibliographic Details
Main Authors:	TAN, Ah-hwee, ONG, Yew-Soon, TAPANUJ, Akejariyawong
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2011
Subjects:	BDI architecture Reinforcement learning Plan learning Self-organizing neural networks Minefield navigation Computer and Systems Architecture Databases and Information Systems Software Engineering
Online Access:	https://ink.library.smu.edu.sg/sis_research/5244 https://ink.library.smu.edu.sg/context/sis_research/article/6247/viewcontent/1_s2.0_S0957417411000650_main.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Singapore Management University
Language:	English

Similar Items

A self-organizing neural architecture integrating desire, intention and reinforcement learning
by: TAN, Ah-hwee, et al.
Published: (2010)

Intentional learning agent architecture
by: SUBAGDJA, Budhitama, et al.
Published: (2009)

Self-organizing neural architectures and cooperative learning in a multiagent environment
by: XIAO, Dan, et al.
Published: (2007)

Integrated cognitive architectures: A survey
by: CHONG, Hui-Qing, et al.
Published: (2009)

A hybrid architecture combining reactive plan execution and reactive learning
by: KARIM, Samin, et al.
Published: (2006)

A self-organizing neural network architecture for intentional planning agents
by: SUBAGDJA, Budhitama, et al.
Published: (2009)

Towards autonomous behavior learning of non-player characters in games
by: FENG, Shu, et al.
Published: (2016)

Off-policy reinforcement learning for efficient and effective GAN architecture search
by: YUAN, Tian, et al.
Published: (2020)

Self-organizing neural models integrating rules and reinforcement learning
by: TENG, Teck-Hou, et al.
Published: (2008)

Self-organizing neural networks integrating domain knowledge and reinforcement learning
by: TENG, Teck-Hou, et al.
Published: (2015)

A Survey on Visual Navigation for Artificial Agents with Deep Reinforcement Learning
by: Zeng, F., et al.
Published: (2021)

Adaptive computer‐generated forces for simulator‐based training, Expert Systems with Applications
by: TENG, Teck-Hou, et al.
Published: (2013)

Time constraint agents? coordination and learning in cooperative multi-agent system
by: WU XUE
Published: (2013)

Hierarchical reinforcement learning with integrated discovery of salient subgoals
by: PATERIA, Shubham, et al.
Published: (2020)

Integrating motivated learning and k-winner-take-all to coordinate multi-agent reinforcement learning
by: TENG, Teck-Hou, et al.
Published: (2014)

Teaching adult learners on software architecture design skills
by: OUH, Eng Lieh, et al.
Published: (2018)

Reinforcement learning of multiple tasks using a hierarchical CMAC architecture
by: Tham, C.K.
Published: (2014)

Reinforcement learning of multiple tasks using a hierarchical CMAC architecture
by: Tham, C.K.
Published: (2014)

Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
by: WANG, Peng, et al.
Published: (2018)

Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
by: Wang, Peng, et al.
Published: (2019)

Scalable multi-agent reinforcement learning for aggregation systems
by: VERMA, Tanvi
Published: (2020)

Towards explaining sequences of actions in multi-agent deep reinforcement learning models
by: KHAING, Phyo Wai, et al.
Published: (2023)

HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning
by: GENG, Minghong, et al.
Published: (2024)

Knowledge-based exploration for reinforcement learning in self-organizing neural networks
by: TENG, Teck-Hou, et al.
Published: (2012)

Motivated learning as an extension of reinforcement learning
by: STARZYK, Janusz, et al.
Published: (2010)

End-to-end deep reinforcement learning for multi-agent collaborative exploration
by: CHEN, Zichen, et al.
Published: (2019)

Exploring experiential learning model and risk management process for an undergraduate software architecture course
by: OUH, Eng Lieh, et al.
Published: (2018)

Interactive teachable cognitive agents: Smart building blocks for multiagent systems
by: SUBAGDJA, Budhitama, et al.
Published: (2016)

S-MADE: Interactive storytelling architecture through goal execution and decomposition
by: CAI, Yundong, et al.
Published: (2007)

“THAT WHICH IS APPARENTLY GLOBAL SHOULD BE REALLY GUIDED REPEATED LOCAL”: A NEW PERSPECTIVE FOR LARGE-SCALE AUTONOMOUS NAVIGATION
by: GAO WEI
Published: (2023)

Goal modelling for deep reinforcement learning agents
by: Leung, Jonathan, et al.
Published: (2022)

Motivated learning for the development of autonomous agents
by: STARZYK, Janusz A., et al.
Published: (2012)

GFANC-RL: reinforcement learning-based generative fixed-filter active noise control
by: Luo, Zhengding, et al.
Published: (2024)

Cooperative reinforcement learning in topology-based multi-agent systems
by: XIAO, Dan, et al.
Published: (2011)

Extreme learning machine terrain-based navigation for unmanned aerial vehicles
by: KAN, Ee May, et al.
Published: (2012)

Integrating temporal difference methods and self‐organizing neural networks for reinforcement learning with delayed evaluative feedback
by: TAN, Ah-hwee, et al.
Published: (2008)

Leveraging long short-term user preference in conversational recommendation via multi-agent reinforcement learning
by: DENG, Yang, et al.
Published: (2023)

BIOLOGICALLY PLAUSIBLE COMPUTATIONS UNDERLYING ONE-SHOT LEARNING OF PAIRED ASSOCIATIONS
by: M GANESHKUMAR
Published: (2023)

End-to-end hierarchical reinforcement learning with integrated subgoal discovery
by: PATERIA, Shubham, et al.
Published: (2022)

Approximate difference rewards for scalable multigent reinforcement learning
by: SINGH, Arambam James, et al.
Published: (2021)