The architecture uses dynamic models of emotions and personality encoded as Bayesian networks to 1) diagnose the emotions and personality of the user, and 2) generate appropriate behavior by an automated agent. Classes of interaction that are interpreted and/or generated include such things as choice of wording, characteristics of speech (speed and pitch), gesture, and facial expression. In particular, we describe the structure of dynamic Bayesian networks (DBNs) that form the basis for the interpretation and generation, and address assessment and calibration of static and dynamic components.
The star-studded night culminated with Sir Mo Farah being named BBC Sports Personality of the Year 2017, but there were plenty of other talking points throughout the show in Liverpool's Echo Arena. Here's how the night unfolded on social media. Let's start with the big news of the night... And then came the congratulations... The star of the night took to Instagram to express his gratitude (and shock, no doubt)...
Probabilistic models were fit to logs of player actions in the card game Dominion in an attempt to find evidence of personality types that could be used to classify player behavior as well as generate probabilistic bot behavior. Expectation Maximization seeded with players' self-assessments for their motivations was run for two different model types — Naive Bayes and a trigram model — to uncover three clusters each. For both model structures, most players were classified as belonging to a single large cluster that combined the goals of splashy plays, clever combos, and effective play, cross-cutting the original categories — a cautionary tale for research that assumes players can be classified into one category or another. However, subjects qualitatively report that the different model structures play very differently, with the Naive Bayes model more creatively combining cards.
We view meeting scheduling as a distributed task whereach agent knows its user's preferences and calendar availability in order to act on behalf of its user. Although we may have some intuitions about how some parameters could affecthe meeting scheduling efficiency and meeting quality, we run several experiments in order to explore the tradeoffs between different parameters. Our experiments show how the calendar and preference privacy affect the efficiency and the meeting joint quality under different experimental scenarios. The resultshow how the meeting scheduling performance is more stable and constant when agents try to keep their calendar and preference information private. We believe that these parameters play a key role in the distributed meeting scheduling task, specially if we are interested in building distributed systems with truly autonomous and independent agents where there is not a fixed control agent. Introduction In our daily life, meeting scheduling is a naturally distributed task which is time-consuming, iterative, and somewhat tedious. It can take place between two persons or among several persons. Sometimes, people just try to schedule one meeting. However, most of the time people need to schedule marly meetings at the same time taking into account several constraints.
The personality of a leader can be used to predict that leader's actions as well as those of the group that he or she leads. However, except for a small number of well-known leaders, the personality of leaders must be inferred from actions and other evidence. We have developed a Bayesian network to infer leader personality variables related to violence from evidence of leader and group actions and the situational demands and context in which the actions occur. The network was applied to a historical situation, and its ability to distinguish extreme personalities was established.