Agent-based simulation of animal behaviour

In this paper it is shown how animal behaviour can be simulated in an agent-based manner. Different models are shown for different types of behaviour, varying from purely reactive behaviour to pro-active, social and adaptive behaviour. The compositional development method for multi-agent systems DESIRE and its software environment supports the conceptual and detailed design, and execution of these models. Experiments reported in the literature on animal behaviour have been simulated for a number of agent models

Deliberate evolution in multi-agent systems

This paper presents an architecture for an agent capable of deliberation about the creation of new agents, and of actually creating a new agent in the multi-agent system, on the basis of this deliberation. After its creation the new agent participates fully in the running multi-agent system. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both structures of agents and (behavioural) properties of agents that can be used as requirements. Moreover, to support the deliberation process the agent has explicit knowledge of relations between structure and properties of agents. To actually create a new agent at run-time on the basis of the results of deliberation, the agent executes a creation action in the material world, which leads to a world state update to include the new agent, after which the new agent functions within the multi-agent system. This approach enables the design of evolution processes in societies of agents for which the evolution is not a merely material process which takes place in isolation from the mental worlds of the agents, but allows for interaction between mental and material processes. A combined mind-matter approach results in which the agents in a society can deliberatively influence the direction of the evolution, comparable to the potential offered by genetic engineering. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation. It is discussed how the approach introduced here can be used as a basis for automatic evolution of multi-agent systems for Electronic Commerce

Autonomous Bidding Coordinated Acceptance in one-to-many negotiations

This work presents the Autonomous Bidding Coordinated Acceptance framework (ABCA): An agent-Team design that allows general bilateral agents to engage in oneto-many negotiations in a setting where (possibly overlapping) deals with multiple opponents are desirable. We propose also a coordinated acceptance strategy that uses the estimated outcomes of its bilateral negotiations while deciding to accept a deal

The challenge of negotiation in the game of Diplomacy

The game of Diplomacy has been used as a test case for complex automated negotiations for a long time, but to date very few successful negotiation algorithms have been implemented for this game. We have therefore decided to include a Diplomacy tournament within the annual Automated Negotiating Agents Competition (ANAC). In this paper we present the setup and the results of the ANAC 2017 Diplomacy Competition and the ANAC 2018 Diplomacy Challenge. We observe that none of the negotiation algorithms submitted to these two editions have been able to significantly improve the performance over a non-negotiating baseline agent. We analyze these algorithms and discuss why it is so hard to write successful negotiation algorithms for Diplomacy. Finally, we provide experimental evidence that, despite these results, coalition formation and coordination do form essential elements of the game

The Likeability-Success Tradeoff: Results of the 2nd Annual Human-Agent Automated Negotiating Agents Competition

We present the results of the 2nd Annual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted 'black-box' agents in human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.</p

Evaluating practical negotiating agents: Results and analysis of the 2011 international competition

This paper presents an in-depth analysis and the key insights gained from the Second International Automated Negotiating Agents Competition (ANAC 2011). ANAC is an international competition that challenges researchers to develop successful automated negotiation agents for scenarios where there is no information about the strategies and preferences of the opponents. The key objectives of this competition are to advance the state-of-the-art in the area of practical bilateral multi-issue negotiations, and to encourage the design of agents that are able to operate effectively across a variety of scenarios. Eighteen teams from seven different institutes competed. This paper describes these agents, the setup of the tournament, including the negotiation scenarios used, and the results of both the qualifying and final rounds of the tournament. We then go on to analyse the different strategies and techniques employed by the participants using two methods: (i) we classify the agents with respect to their concession behaviour against a set of standard benchmark strategies and (ii) we employ empirical game theory (EGT) to investigate the robustness of the strategies. Our analysis of the competition results allows us to highlight several interesting insights for the broader automated negotiation community. In particular, we show that the most adaptive negotiation strategies, while robu

Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league

Enabling negotiating agents to explore very large outcome spaces

This work presents BIDS (Bidding using Diversified Search), an algorithm that can be used by negotiating agents to search very large outcome spaces. BIDS provides a balance between being rapid, accurate, diverse, and scalable search, allowing agents to search spaces with as many as 10^250 possible outcomes on very run-of-the-mill hardware. We show that our algorithm can be used to respond to the three most common search queries employed by 87% of all agents from the Automated Negotiating Agents Competition. Furthermore, we validate one of our techniques by integrating it into negotiation platform GeniusWeb, to enable existing state-of-the-art agents (and future agents) to scale their use to very large outcome spaces