United Kingdom

Value alignment is a crucial aspect of ethical multiagent systems. An important step toward value alignment is identifying values specific to an application context. However, identifying context-specific values is complex and cognitively demanding. To support this process, we develop a methodology and a collaborative web platform that employs AI techniques. We describe this platform, highlighting its intuitive design and implementation.

AAMAS 2021

A Collaborative Platform for Identifying Context-Specific Values

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

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AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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We present an extension of the organizational model and infrastructure adopted in JaCaMo, that explicitly encompasses the notion of exception. We propose an exception handling mechanism for organization management in multi-agent systems. This mechanism relies on abstractions that are seamlessly integrated with organizational concepts, such as responsibilities, goals and norms.

Distributing Responsibilities for Exception Handling in JaCaMo

Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments. Unlike cooperative environments where agents strive towards a common goal, mixed environments are notorious for the conflicts of selfish and social interests. As a consequence, purely rational agents often struggle to maintain cooperation. A prevalent approach to induce cooperative behaviour is to assign additional rewards based on other agents' well-being. However, this approach suffers from the issue of multi-agent credit assignment, which can hinder performance. This issue is efficiently alleviated in cooperative setting with such state-of-the-art algorithms as QMIX and COMA. Still, when applied to mixed environments, these algorithms may result in unfair allocation of rewards. We propose BAROCCO, an extension of these algorithms capable to balance individual and social incentives. The mechanism behind BAROCCO is to train two distinct but interwoven components that jointly affect agents' decisions. We experimentally confirm the advantages of BAROCCO.

Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments

Blockchain-based Distributed Ledgers (DLs) promise to transform the existing financial system by making it truly democratic. In the past decade, blockchain technology has seen many novel applications ranging from the banking industry to real estate. However, in order to be adopted universally, blockchain systems must be scalable to support a high volume of transactions. As we increase the throughput of the DL system, the underlying peer-to-peer network might face multiple levels of challenges to keep up with the requirements. Due to varying network capacities, the slower nodes would be at a relative disadvantage compared to the faster ones, which could negatively impact their revenue. In order to quantify their relative advantage or disadvantage, we introduce two measures of network fairness, $p_f$, the probability of frontrunning and α_f$, the publishing fairness. We show that as we scale the blockchain, both these measures deteriorate, implying that the slower nodes face a disadvantage at higher throughputs. It results in the faster nodes getting more than their fair share of the reward while the slower nodes (slow in terms of network quality) get less. Thus, fairness and scalability in blockchain systems do not go hand in hand.

We might walk together, but I run faster: Network Fairness and Scalability in Blockchains

In this paper, we propose solid semantics  to obtain the most acceptable sets of arguments. Besides, we study the application of solid semantics in the field of judgement aggregation.

Solid Semantics and Extension Aggregation Using Quota Rules under Integrity Constraints

Multi-agent crowd simulation is generally used to construct an environment suitable for reality by parameter estimation. This estimation is to modify a model defined by human movements and behaviors to fit real-world data by means of statistics or optimization [2, 11]. In the field of traffic engineering, for example, parameter estimation is used to modify speed models and collision models of pedestrians and vehicles [10, 13]. For different simulation settings, it is not easy to obtain original data of each real-world environment. Few existing estimation methods have been fairly evaluated. Therefore, there is a need for a benchmark to discuss the applicability of certain estimation methods to other use cases.

MAS-Bench: Parameter Optimization Benchmark for Multi-agent Crowd Simulation

This is a short version of ''Logic-based specification and verification of homogeneous dynamic multi-agent systems'' in Autonomous Agents and Multi-Agent Systems 34, 2 (2020), where we developed a logic-based framework for formal specification and algorithmic verification of homogeneous and dynamic concurrent multi-agent transition systems (HDMAS). Homogeneity means that all agents have the same available actions at any given state and the actions have the same effects regardless of which agents perform them. Dynamicity means that the numbers of controllable and uncontrollable agents may vary throughout the system evolution, possibly at every transition. As a specification language, we extended Alternating-time Temporal Logic to express properties such as: "a coalition of (at least) n controllable agents can ensure against (at most) m uncontrollable agents that any possible evolution of the system satisfies a given objective &#947;", where &#947; is specified again as a formula of that language and each of n and m is either a fixed number or a variable that can be quantified over. Finally, we show the algorithm and worst-case complexity for the model checking problem.

Logic-based Specification and Verification of Homogeneous Dynamic Multi-agent Systems (JAAMAS Track)

We present a logic of evaluation which clarifies the relationship between knowledge, values and preferences of multiple agents in an interactive setting. Evaluation is a fundamental concept for understanding how an ethical agent's decision is affected by her values. We provide a complete axiomatics for the logic and present a dynamic extension by the concept of value expansion. We show that value expansion indirectly affects the agents' preferences by inducing a preference upgrade operation.

A Logic of Evaluation

We present a general framework for modelling and verifying epistemic properties over parameterized multi-agent systems that communicate by truthful public announcements. In our framework, the number of agents or the amount of certain resources are parameterized (i.e. not known a priori), and the corresponding verification problem asks whether a given epistemic property is true regardless of the instantiation of the parameters. For example, in a muddy children puzzle, one could ask whether each child will eventually find out whether (s)he is muddy, regardless of the number of children.  Our framework is regular model checking (RMC)-based, wherein synchronous finite-state automata (equivalently, monadic second-order logic over words) are used to specify the systems. We propose an extension of public announcement logic as specification language. Of special interests is the addition of the so-called iterated public announcement operators, which are crucial for reasoning about knowledge in parameterized systems. Although the operators make the model checking problem undecidable, we show that this becomes decidable when an appropriate "disappearance relation" is given. Further, we show how Angluin's L*-algorithm for learning finite automata can be applied to find a disappearance relation, which is guaranteed to terminate if it is regular. We have implemented the algorithm and apply this to such examples as the Muddy Children Puzzle, the Russian Card Problem, and Large Number Challenge.

Regular Model Checking Approach to Knowledge Reasoning over Parameterized Systems

Multi-player mean-payoff games are a natural formalism to model concurrent and multi-agent systems with self-interested players. Players in such a game traverse a graph, while trying to maximise a mean-payoff function that depends on the plays so generated. As with all games, the equilibria that could arise may have undesirable properties. However, as system designers, we typically wish to ensure that equilibria in such systems correspond to desirable system behaviours, for example, satisfying certain safety or liveness properties. One natural way to do this would be to specify such desirable properties using temporal logic. Unfortunately, the use of temporal logic specifications causes game theoretic verification problems to have very high computational complexity. To this end, we consider ømega-regular specifications, which offer a concise and intuitive way of specifying desirable behaviours of a system. The main results of this work are characterisation and complexity bounds for the problem of determining if there are equilibria that satisfy a given ømega-regular specification in a multi-player mean-payoff game in a number of computationally relevant game-theoretic settings.

Mean-Payoff Games with ω-Regular Specifications

In this paper we introduce a generic approach to solving Sequential Security Games (SGs) based on Evolutionary Algorithms. The method (named EASG) is general and largely game-independent, which allows for its application to a wide range of SGs with only slight adjustments. The efficacy of EASG is verified on 3 types of games from different domains: patrolling, surveillance, and cybersecurity. Comprehensive experiments performed on more than $300$ test games demonstrate robustness and stability of EASG, manifested by repetitively achieving optimal or near-optimal solutions. The main advantage of EASG compared to alternative approaches to solving SGs is its time efficiency. EASG finds high-quality solutions with approximately linear time scalability and can therefore be applied to solving SG instances which are beyond capabilities of the state-of-the-art exact methods. Due to anytime characteristics, EASG is particularly well suited for time-critical applications.

A Collaborative Platform for Identifying Context-Specific Values

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