Strengthening Agents Strategic Ability with Communication

Huang, Xiaowei; Chen, Qingliang; Su, Kaile

doi:10.1609/aaai.v30i1.10121

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Norms are often modelled as the encouragement or prohibition of actions that agents may choose to take by a regulatory agent. The most closely related works in this area are those of [21,31,1], who study the problem of synthesising dynamic norms in different classes of concurrent games to satisfy temporal logic specifications. Whereas norms in these frameworks have the ability to disable actions at runtime, our model confers only the power to incentivise behaviours upon the principal.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Incentive Engineering for Concurrent Games

Hyland

Gutiérrez

Wooldridge

2023

Electron. Proc. Theor. Comput. Sci.

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We consider the problem of incentivising desirable behaviours in multi-agent systems by way of taxation schemes. Our study employs the concurrent games model: in this model, each agent is primarily motivated to seek the satisfaction of a goal, expressed as a Linear Temporal Logic (LTL) formula; secondarily, agents seek to minimise costs, where costs are imposed based on the actions taken by agents in different states of the game. In this setting, we consider an external principal who can influence agents' preferences by imposing taxes (additional costs) on the actions chosen by agents in different states. The principal imposes taxation schemes to motivate agents to choose a course of action that will lead to the satisfaction of their goal, also expressed as an LTL formula. However, taxation schemes are limited in their ability to influence agents' preferences: an agent will always prefer to satisfy its goal rather than otherwise, no matter what the costs. The fundamental question that we study is whether the principal can impose a taxation scheme such that, in the resulting game, the principal's goal is satisfied in at least one or all runs of the game that could arise by agents choosing to follow game-theoretic equilibrium strategies. We consider two different types of taxation schemes: in a static scheme, the same tax is imposed on a state-action profile pair in all circumstances, while in a dynamic scheme, the principal can choose to vary taxes depending on the circumstances. We investigate the main game-theoretic properties of this model as well as the computational complexity of the relevant decision problems.

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Section: Related Work and Conclusionmentioning

confidence: 99%

Incentive Engineering for Concurrent Games

Hyland

Gutiérrez

Wooldridge

2023

Electron. Proc. Theor. Comput. Sci.

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Dependable learning-enabled multiagent systems

Huang

Peng

Zhao

2022

AIC

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We are concerned with the construction, formal verification, and safety assurance of dependable multiagent systems. For the case where the system (agents and their environment) can be explicitly modelled, we develop formal verification methods over several logic languages, such as temporal epistemic logic and strategy logic, to reason about the knowledge and strategy of the agents. For the case where the system cannot be explicitly modelled, we study multiagent deep reinforcement learning, aiming to develop efficient and scalable learning methods for cooperative multiagent tasks. In addition to these, we develop (both formal and simulation-based) verification methods for the neural network based perception agent that is trained with supervised learning, considering its safety and robustness against attacks from an adversarial agent, and other approaches (such as explainable AI, reliability assessment, and safety argument) for the analysis and assurance of the learning components. Our ultimate objective is to combine formal methods, machine learning, and reliability engineering to not only develop dependable learning-enabled multiagent systems but also provide rigorous methods for the verification and assurance of such systems.

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Strengthening Agents Strategic Ability with Communication

Cited by 2 publications

References 24 publications

Incentive Engineering for Concurrent Games

Incentive Engineering for Concurrent Games

Dependable learning-enabled multiagent systems

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