In this paper, we deal with multiobjective integer programming problems involving random variable coefficients in objective functions and/or constraints. After reformulation of them on the basis of a probability maximization model for the chance constrained programming, incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing metho...

A decision maker is presented with two preference orders over n objects and chooses the one which is “closer” to his own preference order. We consider several plausible comparison rules that the decision maker might employ. We show that when n is large and the pair of orders to be compared randomly realizes, different comparison rules lead to statistically almost independent choices. Thus, two ...

This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from realtime market experience and develops explici...

This paper explores a novel approach to linking Graphics and Natural Language Processing (NLP). Our tool, Story Maker, lets users illustrate their stories on the fly, as they enter them on the computer in natural language. Our goals in creating Story Maker are twofold: to explore the use of NLP in the dynamic generation of animated scenes, and to explore ways to exploit users’ input in order to...