The distributionally robust Markov Decision Process approach has been proposed in the literature, where the goal is to seek a distributionally robust policy that achieves the maximal expected total reward under the most adversarial joint distribution of uncertain parameters. In this paper, we study distributionally robust MDP where ambiguity sets for uncertain parameters are of a format that ca...

Introduction In artificial intelligence, decision theory deals with computing a sequence of actions (policy) that an autonomous agent must take in order to optimize its rewards (obtain its goals in the most efficient manner). In many real world situation , an autonomous agent must deal with various sources of uncertainty while computing its optimal policy. In single agent settings, such decisio...

We connect a broad class of generative models through their shared reliance on sequential decision making. Motivated by this view, we develop extensions to an existing model, and then explore the idea further in the context of data imputation – perhaps the simplest setting in which to investigate the relation between unconditional and conditional generative modelling. We formulate data imputati...

In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of service providers populating the environment. Specifically, we propose a generic Bayesian trust model, and formulate the optimal Bayesian solution to the exploration-exploitation problem facing the agents when repeatedly i...

We investigate the problem of using function approximation in reinforcement learning (RL) where the agent’s control policy is represented as a classifier mapping states to actions. The innovation of this paper lies with introducing a measure of state’s decision-making importance. We then use an efficient approximation to this measure as misclassification costs in learning the agent’s policy. As...

Decision making with adaptive utility provides a generalisation to classical Bayesian decision theory, allowing the creation of a normative theory for decision selection when preferences are initially uncertain. The theory of adaptive utility was introduced by Cyert & DeGroot [27], but had since received little attention or development. In particular, foundational issues had not been explored a...

We define a fairness solution criterion for multi-agent decision-making problems, where agents have local interests. This new criterion aims to maximize the worst performance of agents with a consideration on the overall performance. We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy. This game-theoretic approach ...

The sequential probability ratio test (SPRT) and related drift-diffusion model (DDM) are optimal for choosing between two hypotheses using the minimal (average) number of samples and relevant for modeling the decision-making process in human observers. This work extends these models to group decision making. Previous works have focused almost exclusively on group accuracy; here, we explicitly a...

We investigated the contribution of cognitive ability and affect to age differences in sequential decision making by asking younger and older adults to shop for items in a computerized sequential decision-making task. Older adults performed poorly compared to younger adults partly due to searching too few options. An analysis of the decision process with a formal model suggested that older adul...

in this article, decisions about price and stock allocation for a seller with multiple customer classes are analyzed. with each customer arrival, the seller needs to decide about accepting or rejecting the customer’s demand by considering the stock on hand. in the case of acceptance, one needs to decide about the selling price. after any change in the inventory level, decision about continuing ...