We propose a formalism to model and reason about reconfigurable multi-agent systems. In our formalism, agents interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. Inspired by existing multi-robot systems, we represent a system as a set of agents (each

Simulation has been applied to diverse domains such as urban growth modeling and market dynamics modeling. Some of these applications may require validations, based on some real-world observations modeled in the simulation. This validation can be conducted as either qualitative face-validation or quantitative empirical validation; however, as the importance and accumulation of data grows, the importance

This paper investigates the use of group goals and plans as programming abstractions that provide explicit constructs for goals and plans involving coordinated action by groups of agents, with a focus on the BDI agent model. We define a group goal construct, which specifies subgoals that the group members must satisfy for the group goal to succeed, subject to timeouts on the members beginning work

Deep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations

Decision makers can often be confronted with the need to select a subset of objects from a set of candidate objects by just counting on preferences regarding the objects’ features. Here we formalise this problem as the dominant set selection problem. Solving this problem amounts to finding the preferences over all possible sets of objects. We accomplish so by: (i) grounding the preferences over features

We investigate the computational complexity of electoral control in elections. Electoral control describes the scenario where the election chair seeks to alter the outcome of the election by structural changes such as adding, deleting, or replacing either candidates or voters. Such control actions have been studied in the literature for a lot of prominent voting rules. We complement those results by

We study the classic public project problem, where the agents decide whether or not to build a non-excludable public project. We focus on efficient, strategy-proof, and weakly budget-balanced mechanisms (VCG redistribution mechanisms). Our aim is to maximize the worst-case efficiency ratio—the worst-case ratio between the achieved total utility and the first-best maximum total utility. Previous studies

Fair division of land is an important practical problem that is commonly handled either by hiring assessors or by selling and dividing the proceeds. A third way to divide land fairly is via algorithms for fair cake-cutting. Such un-intermediated methods are not only cheaper than an assessor but also theoretically fairer since they guarantee each agent a fair share according to his/her value function

Shortlisting of candidates—selecting a group of “best” candidates—is a special case of multiwinner elections. We provide the first in-depth study of the computational complexity of strategic voting for shortlisting based on the perhaps most basic voting rule in this scenario, \(\ell \)-Bloc (every voter approves \(\ell \) candidates). In particular, we investigate the influence of several different

Markets for zero-day exploits (software vulnerabilities unknown to the software vendor) have a long history and a growing popularity. We study these markets from a revenue-maximizing mechanism design perspective. We first propose a theoretical model for zero-day exploits markets. In our model, one exploit is being sold to multiple buyers. There are two kinds of buyers, which we call the defenders and

We consider a distributed meeting scheduling problem where agents negotiate with each other to reach a consensus over the starting time of the meeting. Each agent has a private preference over a set of time slots, and aims to select its own preferred slot while revealing as little information about its preference as possible. A key challenge in this canonical setting is whether it is possible to design

We study fair resource allocation when the resources contain a mixture of divisible and indivisible goods, focusing on the well-studied fairness notion of maximin share fairness (MMS). With only indivisible goods, a full MMS allocation may not exist, but a constant multiplicative approximate allocation always does. We analyze how the MMS approximation guarantee would be affected when the resources

Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, particularly for on-policy methods. Previous exploration methods either rely on complex structure to estimate the novelty of states, or incur sensitive hyper-parameters causing instability. We propose an efficient exploration method

Ontology alignments enable agents to communicate while preserving heterogeneity in their knowledge. Alignments may not be provided as input and should be able to evolve when communication fails or when new information contradicting the alignment is acquired. The Alignment Repair Game (ARG) has been proposed for agents to simultaneously communicate and repair their alignments through adaptation operators

A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is the artificial agent that learns and acts, it is still up to humans to specify the particular task to be performed. Classical task-specification approaches typically involve humans providing stationary reward functions or

The role of intelligent agents becomes more social as they are expected to act in direct interaction, involvement and/or interdependency with humans and other artificial entities, as in Human-Agent Teams (HAT). The highly interdependent and dynamic nature of teamwork demands correctly calibrated trust among team members. Trust violations are an inevitable aspect of the cycle of trust and since repairing

This special issue of autonomous agents and multi-agent systems sought research articles that discuss relevant topics for the current trends in research on software agents and agent-based software systems. Topics of interest included techniques and technologies to engineer agent-based software systems, as well as languages, frameworks, and infrastructures broadly related to software agents and agent-based

Automated Negotiation is a growing area of research in recent years as it provides a mechanism for intelligent agents representing people and institutions to coordinate their behavior in a complex environment under rational selfish assumptions. Most research in this area assumes either a single negotiation thread with a well-defined utility function for each agent involved or a set of concurrent negotiations

Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their learning power to address the problems on which they fail. In this work, we empirically investigate the learning

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration

In hedonic games, players form coalitions based on individual preferences over the group of players they could belong to. Several concepts to describe the stability of coalition structures in a game have been proposed and analysed in the literature. However, prior research focuses on algorithms with time complexity that is at least linear in the input size. In the light of very large games that arise

Belief propagation algorithms including Max-sum and its variants are important methods for multi-agent optimization. However, they face a significant scalability challenge as the computational overhead grows exponentially with respect to the arity of each utility function. To date, a number of acceleration algorithms for belief propagation algorithms were proposed. These algorithms maintain a lower

The recent massive proliferation of affiliate marketing suggests a new e-commerce paradigm which involves sellers, affiliates and the platforms that connect them. In particular, the fact that prospective buyers may become acquainted with the promotion through more than one affiliate to whom they are connected calls for new mechanisms for compensating affiliates for their promotional efforts. In this

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative

We build on an emerging line of work which studies strategic manipulations in training data provided to machine learning algorithms. Specifically, we focus on the ubiquitous task of linear regression. Prior work focused on the design of strategyproof algorithms, which aim to prevent such manipulations altogether by aligning the incentives of data sources. However, algorithms used in practice are often

This paper focuses on the problem of fairly and efficiently allocating resources to agents. We consider a specific setting, usually referred to as a housing market, where each agent must receive exactly one resource (and initially owns one). In this framework, in the domain of linear preferences, the Top Trading Cycle (TTC) algorithm is the only procedure satisfying Pareto-optimality, individual rationality

Interactive Narrative is an emerging application of automated planning, in which a planning domain is used to generate a consistent chain of narrative actions that constitute a plot structure. The task of creating narrative planning domains has been identified as a bottleneck which is hampering further development of the field. This stems from the difficulties faced by humans authoring such planning

We propose a new method for learning compact state representations and policies separately but simultaneously for policy approximation in vision-based applications such as Atari games. Approaches based on deep reinforcement learning typically map pixels directly to actions to enable end-to-end training. Internally, however, the deep neural network bears the responsibility of both extracting useful

Computational argumentation has taken a predominant place in the modeling of negotiation dialogues over the last years. A competent agent participating in a negotiation process is expected to decide its next move taking into account an, often incomplete, model of its opponent. This work provides a complete computational account of argumentation-based negotiation under incomplete opponent profiles.

Microscopic agent-based traffic simulation is an important tool for the efficient and safe resolution of various traffic challenges accompanying the introduction of autonomous vehicles on the roads. Both the variety of questions that can be asked and the quality of answers provided by simulations, however, depend on the underlying models. In mixed traffic, the two most critical models are the models

In multi-type resource allocation (MTRA) problems, there are \(d\ge 2\) types of items, and n agents who each demand one unit of items of each type and have strict linear preferences over bundles consisting of one item of each type. For MTRAs with indivisible items, our first result is an impossibility theorem that is in direct contrast to the single type (\(d=1\)) setting: no mechanism, the output

In Coalition Structure Generation (CSG), one seeks to form a partition of a given set of agents into coalitions such that the sum of the values of each coalition is maximized. This paper introduces a model for Probabilistic CSG (PCSG), which extends the standard CSG model to account for the stochastic nature of the environment, i.e., when some of the agents considered at start may be finally defective

Recently, it has been proposed that Game Description Language (GDL) could be used to define negotiation domains. This would open up an entirely new, declarative, approach to Automated Negotiations in which a single algorithm could negotiate over any domain, as long as that domain is expressible in GDL. However, until now, the feasibility of this approach has only been demonstrated on a few toy-world

Since its dawn as a discipline, Artificial Intelligence (AI) has focused on mimicking the human mental processes. As AI applications matured, the interest for employing them into real-world complex systems (i.e., coupling AI with Cyber-Physical Systems—CPS) kept increasing. In the last decades, the multi-agent systems (MAS) paradigm has been among the most relevant approaches fostering the development

Police officers conduct routine patrols and perform tasks in response to reported incidents. The importance of each task varies from low (e.g. noise complaint) to high (e.g. murder). The workload associated with each task, indicating the amount of work to be completed for the incident to be processed, may vary as well. Multiple officers with heterogeneous skills may work together on important tasks

Multi-agent systems (MAS) represent a distributed computing paradigm well suited to tackle today’s challenges in the field of the Internet of Things (IoT). Both share many similarities such as the interconnection of distributed devices and their cooperation. The combination of MAS and IoT would allow the transfer of the experience gained in MAS research to the broader range of IoT applications. The

Group decisions are often complicated by a deadline. For example, in committee hiring decisions the deadline might be the next start of a budget, or the beginning of a semester. It may be that if no candidate is supported by a strong majority, the default is to hire no one - an option that may cost dearly. As a result, committee members might prefer to agree on a reasonable, if not necessarily the

A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for

Image sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and specify a privacy setting for each image that they upload. This is difficult for two main reasons: first, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their

In many distributed systems, from cloud to sensor networks, different configurations impact system performance, while strongly depending on the network topology. Hence, topological changes may entail costly reconfiguration and optimisation processes. This paper proposes a multi-agent solution for recovering networks from node failures. To preserve the network topology, the proposed approach relies

We consider tournaments played by a set of players in order to establish a ranking among them. We introduce the notion of irrelevant match, as a match that does not influence the ultimate ranking of the involved parties. After discussing the basic properties of this notion, we seek out tournaments that have no irrelevant matches, focusing on the class of tournaments where each player challenges each

Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from observing its behavior on a task. It inverts RL which focuses on learning an agent’s behavior on a task based on the reward signals received. IRL is witnessing sustained attention due to promising applications in robotics, computer games, and finance, as well as in other sectors. Methods for IRL have, for

Optimal placement of charging stations for electric vehicles (EVs) is critical for providing convenient charging service to EV owners and promoting public acceptance of EVs. There has been a lot of work on EV charging station placement, yet EV drivers’ charging strategy, which plays an important role in deciding charging stations’ performance, is missing. EV drivers make choice among charging stations

One challenging problem in multiagent systems is to cooperate or compete with non-stationary agents that change behavior from time to time. An agent in such a non-stationary environment is usually supposed to be able to quickly detect the other agents’ policy during online interaction, and then adapt its own policy accordingly. This article studies efficient policy detecting and reusing techniques

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as

Unfortunately, a post-galley copyediting error altered the contents of cells in the Condorcet Elections columns of the table in Footnote 7.

We introduce personalised rating, a network-based rating system where individuals, connected in a social network, decide whether or not to consume a service (e.g., a restaurant) based on the evaluations provided by their peers. We compare personalised rating with the more widely used objective rating where, instead, customers receive an aggregate evaluation of what everybody else has declared so far

This paper addresses a task in Interactive Task Learning (Laird et al. IEEE Intell Syst 32:6–21, 2017). The agent must learn to build towers which are constrained by rules, and whenever the agent performs an action which violates a rule the teacher provides verbal corrective feedback: e.g. “No, red blocks should be on blue blocks”. The agent must learn to build rule compliant towers from these corrections

The classical Stable Roommates problem is to decide whether there exists a matching of an even number of agents such that no two agents which are not matched to each other would prefer to be with each other rather than with their respectively assigned partners. We investigate Stable Roommates with complete (i.e., every agent can be matched with any other agent) or incomplete preferences, with ties

Control and manipulation are two of the most studied types of attacks on elections. In this paper, we study the complexity of control attacks on elections in which there are manipulators. We study both the case where the “chair” who is seeking to control the election is allied with the manipulators, and the case where the manipulators seek to thwart the chair. In the latter case, we see that the order

The Housing Market problem is a widely studied resource allocation problem. In this problem, each agent can only receive a single object and has preferences over all objects. Starting from an initial endowment, we want to reach a certain assignment via a sequence of rational trades. We first consider whether an object is reachable for a given agent under a social network, where a trade between two

Asymmetric distributed constraint optimization problems (ADCOPs) have emerged as an important formalism in multi-agent community due to their ability to capture personal preferences. However, the existing search-based complete algorithms for ADCOPs only exploit local knowledge to calculate lower bounds, which leads to inefficient pruning and prohibits them from solving large scale problems. On the

We consider the design of mechanisms for locating facilities on an interval. There are multiple agents on the interval, each receiving a utility determined by their distances to the facilities. The objectives considered are maximization of social welfare (sum of utilities) and egalitarian welfare (minimum utility). Agents can misreport their locations, and so we require the mechanisms to be strategyproof—no

A relevant application domain for agent-based software is given by mobile and wearable applications. In this context, the impressive progress of technologies in the last decade makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures, such as the Belief–Desire–Intention (BDI) one. Accordingly, in this paper we provide a comprehensive

The importance of Agent-Based Simulation (ABS) as scientific method to generate data for scientific models in general and for informed policy decisions in particular has been widely recognised. However, the important technique of code testing of implementations like unit testing has not generated much research interested so far. As a possible solution, in previous work we have explored the conceptual

A principal needs to elicit the true value of an object she owns from an agent who has a unique ability to compute this information. The principal cannot verify the correctness of the information, so she must incentivize the agent to report truthfully. Previous works coped with this unverifiability by employing two or more information agents and awarding them according to the correlation between their

Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human–robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use these models to differentiate

To achieve system-level properties of a multiagent system, the behavior of individual agents should be controlled and coordinated. One way to control agents without limiting their autonomy is to enforce norms by means of sanctions. The dynamicity and unpredictability of the agents’ interactions in uncertain environments, however, make it hard for designers to specify norms that will guarantee the achievement

We study a variant of the multi-agent path finding (MAPF) problem in which the group of agents are required to stay connected with a supervising base station throughout the execution. In addition, we consider the problem of covering an area with the same connectivity constraint. We show that both problems are PSPACE-complete on directed and undirected topological graphs while checking the existence

Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest when constant communication cannot be assumed. This is common in tasks involving information gathering with multiple independently operating sensor devices that may operate over large physical distances, such as unmanned aerial vehicles, or in communication