MsATL: a Tool for SAT-Based ATL Satisfiability Checking

We present MsATL: the first tool for deciding the satisfiability of Alternating-time Temporal Logic (ATL) with imperfect information. MsATL combines SAT Modulo Monotonic Theories solvers with existing ATL model checkers: MCMAS and STV. The tool can deal with various semantics of ATL, including perfect and imperfect information, and can handle additional practical requirements. MsATL can be applied for synthesis of games that conform to a given specification, with the synthesised game often being minimal

Single Transferable Vote: Incomplete Knowledge and Communication Issues

International audienceSingle Transferable Vote (STV) is used in large political elections around the world. It is easy to understand and has desirable normative properties such as clone-proofness. However, voters need to report full rankings, which can make it less practical than plurality voting. We study ways to minimize the amount of communication required to use single-winner STV. In the first part of the paper, voters are assumed to report their top-k alternatives in a single shot. We empirically evaluate the extent to which STV with truncated ballots approximates STV with full information. We also study the computational complexity of the possible winner problem for top-k ballots. For $k=1$, it can be solved in polynomial time, but is NP-complete when $k\geq 2$. In the second part, we consider interactive communication protocols for STV. Building on a protocol proposed by Conitzer and Sandholm (2005), we show how we can reduce the amount of communication required in practice. We then study empirically the average communication complexity of these protocols, based on randomly generated profiles, and on real-world election data. Our conclusion is that STV needs, in practice, much less information than in the worst case

Merry hMAS and Happy New Web: A Wish for Standardizing an AI-Friendly Web Architecture for Hypermedia Multi-Agent Systems

International audienceAlthough it was initially an “Information management proposal” the Web really is a successful « application integration platform ». More importantly, it is both. It is a self-documenting hypermedia system for application and information integration, and that makes the Web very special. Just like it is important to propose a Web of linked data and distributed RDF knowledge graphs as an alternative to data silos, the Web must also support an alternative to intelligence silos and thrive to host a wealth of distributed artificial and natural intelligence forming hybrid communities and managing distributed resources. This is a call for hMAS: Hypermedia Multi-Agent System

A Theory of Mind Approach as Test-Time Mitigation Against Emergent Adversarial Communication

Multi-Agent Systems (MAS) is the study of multi-agent interactions in a shared environment. Communication for cooperation is a fundamental construct for sharing information in partially observable environments. Cooperative Multi-Agent Reinforcement Learning (CoMARL) is a learning framework where we learn agent policies either with cooperative mechanisms or policies that exhibit cooperative behavior. Explicitly, there are works on learning to communicate messages from CoMARL agents; however, non-cooperative agents, when capable of access a cooperative team's communication channel, have been shown to learn adversarial communication messages, sabotaging the cooperative team's performance particularly when objectives depend on finite resources. To address this issue, we propose a technique which leverages local formulations of Theory-of-Mind (ToM) to distinguish exhibited cooperative behavior from non-cooperative behavior before accepting messages from any agent. We demonstrate the efficacy and feasibility of the proposed technique in empirical evaluations in a centralized training, decentralized execution (CTDE) CoMARL benchmark. Furthermore, while we propose our explicit ToM defense for test-time, we emphasize that ToM is a construct for designing a cognitive defense rather than be the objective of the defense.Comment: 6 pages, 7 figure

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

Selfishness Level Induces Cooperation in Sequential Social Dilemmas

A key contributor to the success of modern societies is humanity’s innate ability to meaningfully cooperate. Modern game-theoretic reasoning shows however, that an individual’s amenity to cooperation is directly linked with the mechanics of the scenario at hand. Social dilemmas constitute a subset of particularly thorny such scenarios, typically modelled as normal-form or sequential games, where players are caught in a dichotomy between the decision to cooperate with teammates or to defect, and further their own goals. In this work, we study such social dilemmas through the lens of ’selfishness level’, a standard game-theoretic metric which quantifies the extent to which a game’s payoffs incentivize defective behaviours.The selfishness level is significant in this context as it doubles as a prescriptive notion, describing the exact payoff modifications necessary to induce players with prosocial preferences. Using this framework, we are able to derive conditions, and means, under which normal-form social dilemmas can be resolved. We also produce a first-step towards extending this metric to Markov-game or sequential social dilemmas with the aim of quantitatively measuring the magnitude to which such environments incentivize selfish behaviours. Finally, we present an exploratory empirical analysis showing the positive effects of using a selfishness level directed reward shaping scheme in such environments