Abstract Information about user preferences plays a key role in automated decision making.
In many domains it is desirable to assess such preferences in a qualitative rather than …

In many domains it is desirable to assess the preferences of users in a qualitative rather than
quantitative way. Such representations of qualitative preference orderings form an important …

As people increasingly rely on interactive decision support systems to choose products and
make decisions, building effective interfaces for these systems becomes more and more …

This article provides an overview of the field of qualitative decision theory: its motivating
tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies …

While constraints are ubiquitous in artificial intelligence and constraints are also commonly
used in machine learning and data mining, the problem of learning constraints from …

Preference elicitation is a central problem in AI, and has received significant attention in
single-agent settings. It is also a key problem in multiagent systems, but has received little …

In the late 1990s, AI witnessed an increasing use of the term'argumentation'within its
bounds: in natural language processing, in user interface design, in logic programming and …

The ability to make decisions and to assess potential courses of action is a corner-stone of
many AI applications, and usually this requires explicit information about the decision-maker …

Preference elicitation and preference representation play an important role in any effort to
automate decision making. Unfortunately, few tools exist for preference elicitation that allow …

The majority of real-world probabilistic systems are used by more than one user, thus a utility
model must be elicited separately for each new user. Utility elicitation is long and tedious …