This paper presents an in-depth analysis and the key insights gained from the Second International Automated Negotiating Agents Competition (ANAC 2011). A post-tournament analysis is also included in this paper, to help draw important lessons regarding the strengths and weaknesses of the various strategies used in the PowerTAC-2020 competition. The strategy is improved by making effective use of a forecasting module that seeks to predict upcoming peaks in demand, since in such intervals incurred costs significantly increase. The approach also incorporates a wholesale market strategy that employs Monte Carlo Tree Search to determine TUC-TAC’s best course of action when participating in the market’s double auctions.
In this paper, we present a novel trading strategy that, based on this observation, aims to balance gains against costs and was utilized by the champion of the PowerTAC-2020 tournament, TUC-TAC. Thus, agents that aim to take over a disproportionately high share of the market, often end up with losses due to being obliged to pay huge transmission capacity fees. Typically, the gains of agents increase as the number of their customers rises, but in parallel, costs also increase as a result of higher transmission fees that need to be paid by the electricity broker. The PowerTAC competition provides a multi-agent simulation platform for electricity markets, in which intelligent agents acting as electricity brokers compete with each other aiming to maximize their profits. game theoretic equilibrium or off equilibrium paths), which makes EA^2 easy to generalize to many other games. Our strategy models behaviours of its opponents, rather than situations of the game (e.g. This approach is designed to be adaptive to various types of opponents such that coordination is almost always achieved, which yields consistently high utilities to our agent, as evidenced by the Tournament results and our
To do this, we classify the behaviour of our opponents using the history of joint interactions in order to identify the best player to coordinate with and how this coordination should be established. To receive a high utility in this game, our strategy, EA^2, attempts to find a suitable partner with which to coordinate and exploit the third player. Player chooses a location for their lemonade stand on an island with the aim of being as far as possible from its opponents. The LSG is a repeated symmetric 3–player constant–sum finite horizon game, in which a We describe the winning strategy of the inaugural Lemonade Stand Game (LSG) Tournament.
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