In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

-This study proposes a framework of Uncertainty-based Group Decision Support System (UGDSS). It provides a platform for multiple criteria decision analysis in six aspects including (1) decision environment, (2) decision problem, (3) decision group, (4) decision conflict, (5) decision schemes and (6) group negotiation. Based on multiple artificial intelligent technologies, this framework provides reliable support for the comprehensive manipulation of applications and advanced decision approaches through the design of an integrated multi-agents architecture.
Towards a Reliable Framework of Uncertainty-based Group Decision Support System

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classi­ cal notion of Value of Informationthe expected im­ provement in future decision quality arising from the tnformation acquired by exploration. Estimating this quantity requires an assessment of the agent's uncer­ tainty about its current value estimates for states. In this paper we investigate ways to represent and rea­ son about this uncertainty in algorithms where the sys­ tem attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q­ values based on these. These distributions are used to compute a myopic approximation to the value of infor­ mation for each action and hence to select the action that best balances exploration and exploitation.
Model based Bayesian Exploration

Square grids are commonly used in robotics and game development to model an agent’s environment, and well known in Artificial Intelligence heuristic search algorithms (A*, JPS, Theta* etc.) are utilized for grid path planning. A lot of research in this area has been focused so far on finding the shortest paths while in many applications producing smooth paths is preferable. In our work, we study the problem of generating smooth grid paths and concentrate on angle constrained path planning. We put angle constrained path planning problem formally and present a new algorithm of solving it – LIAN. We examine LIAN both theoretically and empirically. On the theoretical side, we prove that LIAN is sound and complete (under well-defined restrictions). On the experimental side, we show that LIAN significantly outperforms competitors in ability to find solutions under tough resource constraints and in computational efficiency.

Grid-based angle-constrained path planning