Is human-like and well playing contradictory for Diplomacy bots?

Kemmerling, Markus; Ackermann, Niels; Beume, Nicola; Preuß, Mike; Uellenbeck, Sebastian; Walz, Wolfgang

doi:10.1109/cig.2009.5286472

Cited by 8 publications

(9 citation statements)

References 2 publications

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“…Given the wide availability of human-based Diplomacy tournaments 5 , some authors (such as Kemmerling et al [10]) have devoted attention to develop human-like playing behavior, for which opponent modeling techniques play an important role. Developing agents that learn autonomously to play Diplomacy has also received attention from the research community.…”

Section: Related Workmentioning

confidence: 99%

Strategic Negotiation and Trust in Diplomacy – The DipBlue Approach

Ferreira¹,

Cardoso

Reis

2015

Transactions on Computational Collective Intelligence XX

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Abstract. 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.

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Section: Related Workmentioning

confidence: 99%

Strategic Negotiation and Trust in Diplomacy – The DipBlue Approach

Ferreira¹,

Cardoso

Reis

2015

Transactions on Computational Collective Intelligence XX

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“…With a capacity of one for every edge and slightly changed update rules, one obtains rules that resemble the board game Diplomacy very closely. This game was invented by Calhamer in 1959, also see [16], and has attracted some interest already in the CI in games community [17], [18] due to its strategic variability despite simple rules.…”

Section: Discussionmentioning

confidence: 99%

RedTNet: A network model for strategy games

Hingston

Preuß

Spierling

2010

IEEE Congress on Evolutionary Computation

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Abstract-In this work, we develop a simple, graph-based framework, RedTNet, for computational modeling of strategy games and simulations. The framework applies the concept of red teaming as a means by which to explore alternative strategies. We show how the model supports computer-based red teaming in several applications: realtime strategy games and critical infrastructure protection, using an evolutionary algorithm to automatically detect good and often surprising strategies.

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“…As an example of the influence of the type of information required, creating a believable bot for a complex strategy game such as Civilization is hard, although there are moderately successful attempts; however, creating a believable bot for a digital version of the strategy game Diplomacy is orders of magnitude harder, even though the action space of the latter game is smaller [7]. The key difference here is that text-based (or verbal) communication between players is crucial in Diplomacy, whereas it is often not even possible in Civilization.…”

Section: What Is Believability?mentioning

confidence: 99%

Assessing Believability

et al. 2012

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We discuss what it means for a non-player character (NPC) to be believable or human-like, and how we can accurately assess believability. We argue that participatory observation, where the human assessing believability takes part in the game, is prone to distortion effects. For many games, a fairer (or at least complementary) assessment might be made by an external observer that does not participate in the game, through comparing and ranking the performance of human and non-human agents playing a game. This assessment philosophy was embodied in the Turing Test Track of the recent Mario AI Championship, where non-expert bystanders evaluated the humanlikeness of several agents and humans playing a version of Super Mario Bros. We analyze the results of this competition. Finally, we discuss the possibilities for forming models of believability and of maximizing believability through adjusting game content rather than NPC control logic.

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Is human-like and well playing contradictory for Diplomacy bots?

Cited by 8 publications

References 2 publications

Strategic Negotiation and Trust in Diplomacy – The DipBlue Approach

Strategic Negotiation and Trust in Diplomacy – The DipBlue Approach

RedTNet: A network model for strategy games

Assessing Believability

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