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

This booklet 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 conscientiously reviewed and chosen for inclusion within the e-book. furthermore, the quantity lists the 34 posters that have been on demonstrate throughout the convention. The papers are geared up in topical sections named: cognitive versions; functions; discussion, language, speech; non-verbal behaviour; and social, cultural versions and agents.

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1007– 1014 (2012) 27. : Elements of Episodic Memory. Clarendon Press, Oxford (1983) ˇ 28. : Towards modeling false memory using virtual characters: a position paper. In: Symposium on Human Memory for Artificial Agents, AISB, pp. sg Abstract. Three key requirements of realistic characters or agents in virtual world can be identified as autonomy, interactivity, and personification. Working towards these challenges, this paper proposes a brain inspired agent architecture that integrates goal-directed autonomy, natural language interaction and humanlike personification.

The input signals are typically represented as a set of vectors of values in the perceptual buffer, taken from the sensors. – The Motor Module receives and executes the actions, produced by a readout action from the core inference area. The actions are typically represented as a set of discrete values in the motor buffer, each of which denotes one of the possible actions. – The Intentional Module consists of the task-relevant goals serving as the context. Each goal is represented as a target state vector in the goal buffer, representing the active goals of the agent.

At the end of the day, he retrospectively encodes the whole experience. The strategy memorizes the value of variable X such that: X ← arg maxY ∈V OI KL (PM (Y |P er0:T ) → PM (Y |mems)), where P (Y |P er0:T ) ≡ P (Y |X = fX : fX ∈ P er0:T ); we condition the probability on all observations. V OI ⊆ V is a set of random variables of interest whose value can DyBaNeM: Bayesian Episodic Memory Framework for IVAs 23 0:n be remembered by the model. In our implementation V OI = E0:T ∪ O0:T . Note that we remember only the most probable value, including the time index.

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