Handling preferences [16] is important in a number of domains, including recommender systems, computational advertisement, personal cognitive assistants, systems for decision support (for example, in medicine) and robotics. Artificial intelligence has been dealing with preferences for quite some time. In order to get closer to the goal of realizing autonomous agents that can decide and act on b...

Preference modeling has a crucial role in customer relationship management systems. Traditional approaches to preference modeling are based on decision and utility theory by explicitly querying users about the behavior of value function, or utility of every outcome with regard to each decision criterion. They are error-prone and labor intensive. To address these limitations, computer based impl...

We assessed preference for video or in vivo modeling using a concurrent-chains arrangement with 3 children with autism. The two modeling conditions produced similar acquisition rates and no differential selection (i.e., preference) for all 3 participants.

1. Introduction With the maturity of the consumers' life, its importance is growingly recognized to measure and characterize their diversified preference of consumers. Here, we extend Thurstone's classical model (1927) expressing the discrimination of two stimuli and develop a unified maximum likelihood procedure. It can analyze various types of preference test and effective when fairly a large...

Many realistic decision aid problems are fraught with facets of ambiguity, uncertainty and conflict, which hamper the effectiveness of conventional and fuzzy preference modeling approaches, and command the use of more expressive representations. In the past, some authors have already identified Ginsberg’s/Fitting’s theory of bilattices as a naturally attractive candidate framework for represent...