By Timothy Bickmore, Stacy Marsella, Candace Sidner
This booklet constitutes the lawsuits of the 14th overseas convention on clever digital brokers, IVA 2014, held in Boston, MA, united states, in August 2014. The 14 complete and 24 brief papers offered have been rigorously reviewed and chosen from seventy eight submissions. additionally, the quantity comprises 25 demo and poster papers which have been on exhibit in the course of the convention. The papers hide many elements of clever digital agent thought and alertness with a unique specialise in their use in healthcare.
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Additional info for Intelligent Virtual Agents: 14th International Conference, IVA 2014, Boston, MA, USA, August 27-29, 2014. Proceedings
Still, there are 26% of answers given that are not suitable to the users requests. 3 Answers Evaluation From the 42 requests that did not return a discarded answer we selected 20 (from now on the test set), with the following criteria: – – – – Do not contain offensive language; From the 9 questions paraphrasing How are you? ”, were rejected; From the remaining requests, we randomly chose them. Then, we built a questionnaire, based on the answers provided by SSS to those 20 requests, and also on hand-crafted plausible answers.
1. One of the advantages of the Agent Framework is its modular structure, where each functionality is encapsulated in a C++ plugin that can be extended or created according to the developers requirements. In this way, 3rd-party libraries (commercial or not) can be integrated, bringing to the framework new functionalities like speech recognition, voice generation, alternative input devices and so on. For regular users, the framework as it is oﬀers an intuitive node-based interface, where the existent functionalities can be visualized as nodes that can be connected among themselves to create the logic of the application.
Then each erc are probabilistically placed into a bin receivedi , based on the type of received emotion and Figure 2. Our probabilistic system will either place the emotion to the bin of the emotion received or the transition bin, with a higher probability of being placed in the transition bin. For example, if an agent is experiencing happiness, and attempts to decode an agent displaying sadness, then the happy agent has a likely chance to become confused, but may also become sad from it. erc = eci ∗ wc (3) Once an agent probabilistically condenses all channels into receivedi , it is added back into the agents emotion, as seen in Equation 4.
Intelligent Virtual Agents: 14th International Conference, IVA 2014, Boston, MA, USA, August 27-29, 2014. Proceedings by Timothy Bickmore, Stacy Marsella, Candace Sidner