Showing papers presented at "European Workshop on Multi-Agent Systems in 2021"

Logic and Model Checking by Imprecise Probabilistic Interpreted Systems

Abstract: Stochastic multi-agent systems raise the necessity to extend probabilistic model checking to the epistemic domain. Results in this direction have been achieved by epistemic extensions of Probabilistic Computation Tree Logic and related Probabilistic Interpreted Systems. The latter, however, suffer of an important limitation: they require the probabilities governing the system’s behaviour to be fully specified. A promising way to overcome this limitation is represented by imprecise probabilities. In this paper we introduce imprecise probabilistic interpreted systems and present a related logical language and model-checking procedures based on recent advances in the study of imprecise Markov processes.

Aiming for Half Gets You to the Top: Winning PowerTAC 2020

Abstract: 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. 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. 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. 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. 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. 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. 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.

Large-scale, dynamic and distributed coalition formation with spatial and temporal constraints

Abstract: The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload. To maximize the number of completed tasks, the agents need to cooperate by forming, disbanding and reforming coalitions. The original mathematical programming formulation of the CFSTP is difficult to implement, since it is lengthy and based on the problematic Big-M method. In this paper, we propose a compact and easy-to-implement formulation. Moreover, we design D-CTS, a distributed version of the state-of-the-art CFSTP algorithm. Using public London Fire Brigade records, we create a dataset with 347588 tasks and a test framework that simulates the mobilization of firefighters in dynamic environments. In problems with up to 150 agents and 3000 tasks, compared to DSA-SDP, a state-of-the-art distributed algorithm, D-CTS completes \(3.79\% \pm [42.22\%, 1.96\%]\) more tasks, and is one order of magnitude more efficient in terms of communication overhead and time complexity. D-CTS sets the first large-scale, dynamic and distributed CFSTP benchmark.

Governing black-box agents in competitive multi-agent systems

Abstract: Competitive Multi-Agent Systems (MAS) are inherently hard to control due to agent autonomy and strategic behavior, which is particularly problematic when there are system-level objectives to be achieved or specific environmental states to be avoided.

Point Based Solution Method for Communicative IPOMDPs.

Abstract: Communicative interactive POMDPs (CIPOMDPs) provide a principled framework for optimal interaction and communication in multi-agent settings by endowing agents with nested models (theories of mind) of others and with the ability to communicate with them. In CIPOMDPs, agents use Bayes update to process their observations and messages without the usual assumption of cooperative discourse. We propose a variant of the point-based value iteration method, called IPBVI-Comm, to compute the approximate optimal policy of a CIPOMDP agent. We then use the IPBVI-Comm to study the optimal communicative behavior of agents in cooperative and competitive scenarios. Unsurprisingly, it is optimal for agents to attempt to mislead if their preferences are not aligned. But it turns out the higher depth of reasoning allows an agent to detect insincere communication and to guard against it. Specifically, in some scenarios, the agent is able to distinguish a truthful friend from a deceptive foe based on the message received.

Ascending-Price Mechanism for General Multi-sided Markets

Abstract: We present an ascending-price mechanism for a multi-sided market with a variety of participants, such as manufacturers, logistics agents, insurance providers, and assemblers. Each deal in the market may consist of a combination of agents from separate categories, and different such combinations are simultaneously allowed. This flexibility lets multiple intersecting markets be resolved as a single global market. Our mechanism is obviously-truthful, strongly budget-balanced, individually rational, and attains almost the optimal gain-from-trade when the market is sufficiently large. We evaluate the performance of the suggested mechanism with experiments on real stock market data and synthetically produced data.