Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game

TL;DR

Agent Madoff is a heuristic-based negotiation AI for the Diplomacy game, combining region evaluation, acceptance, and bidding strategies, which achieved second place at ANAC 2017.

Contribution

It introduces a novel heuristic architecture for negotiation in Diplomacy, integrating evaluation, acceptance, and bidding components for strategic gameplay.

Findings

Achieved 2nd place at ANAC 2017

Demonstrated effective negotiation strategies in Diplomacy

Proposed a modular architecture for negotiation agents

Abstract

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.