Strategic negotiation and trust in diplomacy - the DipBlue approach

The study of games in Artificial Intelligence has a long tradition. Game playing has been a fertile environment for the development of novel approaches to build intelligent programs. Multi-agent systems (MAS), in particular, are a very useful paradigm in this regard, not only because multi-player games can be addressed using this technology, but most importantly because social aspects of agenthood that have been studied for years by MAS researchers can be applied in the attractive and controlled scenarios that games convey. Diplomacy is a multi-player strategic zero-sum board game, including as main research challenges an enormous search tree, the difficulty of determining the real strength of a position, and the accommodation of negotiation among players. Negotiation abilities bring along other social aspects, such as the need to perform trust reasoning in order to win the game. The majority of existing artificial players (bots) for Diplomacy do not exploit the strategic opportunities enabled by negotiation, focusing instead on search and heuristic approaches. This paper describes the development of DipBlue, an artificial player that uses negotiation in order to gain advantage over its opponents, through the use of peace treaties, formation of alliances and suggestion of actions to allies. A simple trust assessment approach is used as a means to detect and react to potential betrayals by allied players. DipBlue was built to work with DipGame, a MAS testbed for Diplomacy, and has been tested with other players of the same platform and variations of itself. Experimental results show that the use of negotiation increases the performance of bots involved in alliances, when full trust is assumed. In the presence of betrayals, being able to perform trust reasoning is an effective approach to reduce their impact. © Springer-Verlag Berlin Heidelberg 2015

Towards general cooperative game playing

Attempts to develop generic approaches to game playing have been around for several years in the field of Artificial Intelligence. However, games that involve explicit cooperation among otherwise competitive players cooperative negotiation games have not been addressed by current approaches. Yet, such games provide a much richer set of features, related with social aspects of interactions, which make them appealing for envisioning real-world applications. This work proposes a generic agent architecture Alpha to tackle cooperative negotiation games, combining elements such as search strategies, negotiation, opponent modeling and trust management. The architecture is then validated in the context of two different games that fall in this category Diplomacy and Werewolves. Alpha agents are tested in several scenarios, against other state-of-the-art agents. Besides highlighting the promising performance of the agents, the role of each architectural component in each game is assessed. (c) Springer International Publishing AG, part of Springer Nature 2018