Agent Madoff：一个基于启发式的外交策略博弈谈判代理
Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game
摘要：
在本文中，我们介绍了代理麦道夫的策略，这是一个基于谈判的代理，在自动谈判代理竞赛（ANAC 2017）中获得第二名。代理麦道夫是实现玩游戏外交，这是一个战略棋盘游戏，模仿第一次世界大战期间的情况。每个玩家代表一个主要的欧洲大国，必须与其他势力谈判，并赢得地图上大多数供应中心的所有权。我们提出了一个设计架构，其中包括3个组件：启发式模块，接受策略和投标策略。启发式模块负责评估图上哪些区域更有价值，考虑区域的类型和与该区域相邻的供应中心的数量，并返回地图上每个区域的效用值。接受策略是根据订单的类型，通过使用复合函数计算接受概率，在个案基础上完成的。投标策略采用防御性方法，旨在中和攻击并解决与其他参与者的冲突，以最大限度地减少我们在供应中心的损失。
英文摘要：
In this paper, we present the strategy of Agent Madoff, which is a heuristic-based negotiation agent that won 2nd place at the Automated Negotiating Agents Competition (ANAC 2017). Agent Madoff is implemented to play the game Diplomacy, which is a strategic board game that mimics the situation during World War I. Each player represents a major European power which has to negotiate with other forces and win possession of a majority supply centers on the map. We propose a design architecture which consists of 3 components: heuristic module, acceptance strategy and bidding strategy. The heuristic module, responsible for evaluating which regions on the graph are more worthy, considers the type of region and the number of supply centers adjacent to the region and return a utility value for each region on the map. The acceptance strategy is done on a case-by-case basis according to the type of the order by calculating the acceptance probability using a composite function. The bidding strategy adopts a defensive approach aimed to neutralize attacks and resolve conflict moves with other players to minimize our loss on supply centers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
