Cooperating agents can make commitments to help each other, but commitments might have to be probabilistic when actions have stochastic outcomes. We consider the additional complication in cases where an agent might prefer to change its policy as it learns more about its reward function from experience. How should such an agent be allowed to change its policy while still faithfully pursuing its...

the sensitivity analysis for multi-attribute decision making (madm) problems is important for two reasons: first, the decision matrix as the source of the results of a decision problem is inaccurate because it sorts the alternatives in each criterion inaccurately. second, the decision maker may change his opinions in a time period because of changes in the importance of the criteria and in the ...

Human-robot interactive decision-making is increasingly becoming ubiquitous, and trust an influential factor in determining the reliance on autonomy. However, it not reasonable to systems that are beyond our comprehension, typical machine learning data-driven black-box paradigms impede interpretability. Therefore, critical establish computational trustworthy mechanisms enhanced by interpretabil...

Successfully managing global climate change will require a process of sequential, or iterative, decision-making, whereby policies and other decisions are revised repeatedly over multiple decades in response to changes in scientific knowledge, technological capabilities, or other conditions. Sequential decisions are required by the combined presence of long lags and uncertainty in climate and en...

The paper presents a methodology for building sequential decision support systems based on decision theory using value of information (for short, DT-VOI based SDSSs). DT-VOI based SDSSs support decision-makers in difficult problems of sequential decision-making. In particular we consider the problem of buildingDT-VOI based SDSSs which are capable of supporting decisions in critical situations w...

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal predetermin...

Decision making from sequential sampling, especially when more than two alternative choices are possible, requires appropriate stopping criteria to maximize accuracy under time constraints. Optimal conditions for stopping have previously been investigated for modeling human decision making processes. In this work, we show how the k-nearest neighbor classification algorithm in machine learning c...