A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decision-making are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure and models for the expected performance and cost of visual operators. The TEA-1 selective vision system uses Bayes nets for representation and benefitcost analysis for control of visual and non-visual actions. It is the high-level control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA-1 demonstrates that Bayes nets and decision theoretic techniques provide a general, re-usable framework for constructi...

One feature of intelligent user interfaces is an ability to make decisions that take into account a variety of factors, some of which may depend on the current situation. This article focuses on one general approach to such decision making: Predict the consequences of possible system actions on the basis of prior empirical learning, and evaluate the possible actions, taking into account situation-dependent priorities and the tradeoffs between the consequences. This decisiontheoretic approach is illustrated in detail with reference to an example decision problem, for which models for decision making were learned from experimental data. It is shown how influence diagrams and methods of decision-theoretic planning can be applied to arrive at empirically well-founded decisions. This paradigm is then compared with two other paradigms that are often employed in intelligent user interfaces. Finally, various possible ways of learning (or otherwise deriving) suitable decision-theoretic models are dis- cussed.

The ability to generate effective evaluative arguments is critical for systems intended to advise and persuade their users. We have developed a system that generates evaluative arguments that are tailored to the user, properly arranged and concise. We have also devised an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. This paper presents the results of a formal experiment we performed in our framework to verify the influence of user tailoring on argument effectiveness. 1

We propose an argumentation strategy for generating evaluative arguments that can be applied in systems serving as personal assistants or advisors. By following guidelines from argumentation theory and by employing a quantitative model of the user's preferences, the strategy generates arguments that are tailored to the user, properly arranged and concise. Our proposal extends the scope of previous approaches both in terms of types of arguments generated, and in terms of compliance with principles from argumentation theory.

We have developed a system that generates evaluative arguments that are tailored to the user, properly arranged and concise. We have also developed an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. This paper presents the results of a formal experiment we have performed in our framework to verify the influence of argument conciseness on argument effectiveness

Several methods for the processing of incomplete preference information in additive preference models have been proposed. Due to the lack of adequate decision aiding tools, however, only a few case studies have been reported so far. In this paper, we present PRIME Decisions, a decision aiding tool which supports the analysis of incomplete preference information with the PRIME method. PRIME Decisions also offers novel features such as decision rules and guided elicitation tours. The application of PRIME Decisions is illustrated with a case study on the valuation of a high-tech company.

The radiation protection authorities have seen a potential for applying multi-attribute risk analysis in nuclear emergency management and planning to deal with the conflicting objectives, different parties involved, and uncertainties. This type of approach is expected to help in at least the following three areas: to ensure that all the relevant attributes are considered in the decision making; to enhance communication between the concerned parties, including the public; and to provide a method for including risk analysis explicitly in the process. A multi-attribute utility theory (MAUT) analysis was used to select a strategy for protecting the population after a simulated nuclear accident. The value-focused approach and the use of a neutral facilitator were seen as useful.

In this paper we demonstrate how principles of Multiple Objective Decision Making (MODM) can be used to analysis, design and implement multiple behavior based systems. A structured methodology is achieved where each system objective, such as obstacle avoidance or convoying, is modeled as a behavior. Using MODM we formulate mechanisms for integrating such behaviors into more complex ones. A mobile robot navigation example is given where the principles of MODM are demonstrated. Simulated as well as real-world experiments show that a smooth blending of behaviors according to the principles of MODM enables coherent robot behavior. Keywords: action-selection mechanisms, multiple-objective decision making, behavior-based, obstacle avoidance, mobile robotics 1. INTRODUCTION A fundamental problem for autonomous agents, e.g. intelligent robotic systems, is to decide what to do next. This problem is in the literature denoted the action selection problem. The following is a definition of the A...

We simulate and analyze pilot decision making in one-on-one air combat using an influence diagram. Unlike most of the existing approaches, an influence diagram graphically describes the factors of a decision process and explicitly handles the decision maker’s preferences under conditions of uncertainty. In the pilot decision model, the possible combat situations related to each maneuver alternative are associated with a probability and a utility. Influence diagram analysis produces a probability distribution of the overall utility that represents the successfulness of a maneuver and gives information to make rational maneuvering decisions. Sensitivity analysis determines the impacts of different factors on the outcome of the maneuvering decision. The effects of sensor information that will reduce the uncertainty of the model are evaluated using Bayesian reasoning. The model can be utilized in the analysis of a single decision situation or as an automated decision making system that selects combat maneuvers in air combat simulators. I.

How can an adaptive intelligent interface decide what particular action to perform in a given situation, as a function of perceived properties of the user and the situation? Ideally, such decisions should be made on the basis of an empirically derived causal model. In this paper we show how such a model can be constructed given an appropriately limited system and domain: On the basis of data from a controlled experiment, an influence diagram for making adaptation decisions is learned automatically. We then discuss why this method will often be infeasible in practice, and how parts of the method can nonetheless be used to create a more solid basis for adaptation decisions. Keywords Adaptive systems, Experiments, Decision theory, Influence diagrams, Bayesian networks