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Download Intelligent Virtual Agents: 13th International Conference, by Weizi (Philip) Li, Tim Balint, Jan M. Allbeck (auth.), Ruth PDF

By Weizi (Philip) Li, Tim Balint, Jan M. Allbeck (auth.), Ruth Aylett, Brigitte Krenn, Catherine Pelachaud, Hiroshi Shimodaira (eds.)

This e-book constitutes the complaints of the thirteenth overseas convention on clever digital brokers, IVA 2013, held in Edinburgh, united kingdom, in August 2013. there has been a complete of ninety four submissions. The 18 complete and 18 brief papers offered during this quantity have been rigorously reviewed and chosen for inclusion within the e-book. additionally, the amount lists the 34 posters which have been on show in the course of the convention. The papers are prepared in topical sections named: cognitive versions; functions; discussion, language, speech; non-verbal behaviour; and social, cultural versions and agents.

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Additional info for Intelligent Virtual Agents: 13th International Conference, IVA 2013, Edinburgh, UK, August 29-31, 2013. Proceedings

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8,24], we use mathematically better rooted deviation from a statistical schema. Fourth, current models cannot express degree of belief in the recalled memory, they usually either return an episode or nothing. Our framework removes this restriction, recall in DyBaNeM results in multiple, more or less probable, possibilities. Fifth, the framework uses IVA’s personal experience in encoding of the episodes. Two IVAs may remember the same episode in differently. From the psychological point of view the framework is inspired by the Fuzzy-Trace Theory (FTT) [11].

Ent representing a hierarchy of Episodic trace 0:n t 0 episodes at time t; et is the currently active lowest level episode, e1t is its direct parent episode and ent is the root episode in the hierarchy of depth n. Example of an episodic trace can be 0:n = W ALK, COM M U T E and 0 0:n = GO BY BU S, COM M U T E . The notation of episodic trace reflects 1 hierarchical nature of agent behavior. Our framework uses probabilistic representation, hence even if there is only one objectively valid episodic trace at each time step, input of the EM model will be a probability distribution.

KL divergence of two PMFs P (X) and P (Y ), where D(X) = D(Y ) is defined as: KL(P (X) → P (Y )) = x∈D(X) P (X = x)ln PP (X=x) (Y =x) . We use notation with → to stress directionality of KL divergence; note that it is not symmetrical. Learning Schemata. Episodic schemata are represented by parameters θˆ of a DBN. Expressiveness of schemata depends on the structure of a model at hand. We suppose that the DBN’s topology is fixed. Thus learning schemata will reduce to well known parameter learning methods.

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