An Architectural Integration of Temporal Motivation Theory for Decision Making (bibtex)
by Paul S Rosenbloom, Volkan Ustun
Abstract:
Temporal Motivation Theory (TMT) is incorporated into the Sigma cognitive architecture to explore the ability of this combination to yield human-like decision making. In conjunction with Lazy Reinforcement Learning (LRL), which provides the inputs required for this form of decision making, experiments are run on a simple reinforcement learning task, a preference reversal task, and an uncertain two-choice task.
Reference:
An Architectural Integration of Temporal Motivation Theory for Decision Making (Paul S Rosenbloom, Volkan Ustun), In In Proceedings of the 17thAnnual Meeting of the International Conference on Cognitive Modeling, 2019.
Bibtex Entry:
@inproceedings{rosenbloom_architectural_2019,
	address = {Montreal, Canada},
	title = {An {Architectural} {Integration} of {Temporal} {Motivation} {Theory} for {Decision} {Making}},
	url = {https://iccm-conference.neocities.org/2019/proceedings/papers/ICCM2019_paper_7.pdf},
	abstract = {Temporal Motivation Theory (TMT) is incorporated into the Sigma cognitive architecture to explore the ability of this combination to yield human-like decision making. In conjunction with Lazy Reinforcement Learning (LRL), which provides the inputs required for this form of decision making, experiments are run on a simple reinforcement learning task, a preference reversal task, and an uncertain two-choice task.},
	booktitle = {In {Proceedings} of the 17thAnnual {Meeting} of the {International} {Conference} on {Cognitive} {Modeling}},
	author = {Rosenbloom, Paul S and Ustun, Volkan},
	month = jul,
	year = {2019},
	keywords = {Virtual Humans, UARC},
	pages = {6}
}
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