Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. ...Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the charaeteristics of the underlying negotiation problem （e.g. on the complexity of participant＇s utility functions, as well as the degree of conflict between participants）. While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by （1） defining a set of scenario metrics to capture the relevant features of negotiation problems, （2） evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, （3） applying machine learning techniques to identify which mechanisms work best with which scenarios, and （4） demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.展开更多
The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood mea...The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study （n = 26） and a controlled study （n = 16） were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response （GSR） data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the re- suits of the studies found significant average correlations be- tween mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is pos- sible from interaction behaviour with keyboard and mouse.展开更多
文摘Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the charaeteristics of the underlying negotiation problem （e.g. on the complexity of participant＇s utility functions, as well as the degree of conflict between participants）. While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by （1） defining a set of scenario metrics to capture the relevant features of negotiation problems, （2） evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, （3） applying machine learning techniques to identify which mechanisms work best with which scenarios, and （4） demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.
文摘The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study （n = 26） and a controlled study （n = 16） were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response （GSR） data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the re- suits of the studies found significant average correlations be- tween mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is pos- sible from interaction behaviour with keyboard and mouse.
