@inproceedings {2005,
	title = {Motivation Driven Learning of Action Affordances},
	booktitle = {Proceedings of the Symposium on Agents that Want and Like: Motivational and Emotional Roots of Cognition and Action (SSAISB{\textquoteright}05)},
	year = {2005},
	note = {<a href="https://aisb.org.uk/wp-content/uploads/2019/12/2_Agents_Final.pdf">Download symposium proceedings</a> (pdf)},
	pages = {33{\textendash}36},
	publisher = {AISB},
	organization = {AISB},
	address = {Hatfield, UK},
	abstract = {Survival in the animal realm often depends on the ability to elucidate the potentialities for action offered by every situation. This paper argues that affordance learning is a powerful ability for adaptive, embodied, situated agents, and presents a motivation-driven method for their learning. The method proposed considers the agent and its environment as a single unit, thus intrinsically relating agent{\textquoteright}s interactions to fluctuations of the agent{\textquoteright}s internal motivation. Being that the motivational state is an expression of the agent{\textquoteright}s physiology, the existing causality of interactions and their effect on the motivational state is exploited as a principle to learn object affordances. The hypothesis is tested in a Webots 4.0 simulator with a Khepera robot.},
	isbn = {1-902956-41-7},
	url = {https://aisb.org.uk/wp-content/uploads/2019/12/2_Agents_Final.pdf},
	author = {Cos-Aguilera, Ignasi and Lola Ca{\~n}amero and Gillian M Hayes},
	editor = {Lola Ca{\~n}amero}
}
