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Disaster response is a major challenge given the social and economic impact on the communities affected by disaster incidents. We investigate how coalition formation can be used for the problem of forming a hierarchy of resources (e.g., personnel responding to the incident). As a case study, we consider the roaring river flood scenario and model the Incident Command System (ICS) framework—providing guidelines on cooperatively responding to disaster incidents. Our approach is based on sequential characteristic-function games induced by size-based valuation structures. We show that this approach can deliver a hierarchy as required by the Operations Section of the ICS and provides a promising way to generate practical solutions for some realistic disaster scenarios. [ABSTRACT FROM AUTHOR]

Task allocation is an important aspect of multiagent coordination. However, there are many challenges in developing appropriate strategies for multiagent teams so that they operate efficiently. Real‐world scenarios such as flooding disasters usually require the use of heterogeneous robots and the execution of tasks with different structures and complexities. In this paper, we propose a decentralized task allocation mechanism considering different types of tasks for heterogeneous agent teams where agents play different roles and carry out tasks according to their own capabilities. We have run several experiments to evaluate the proposed mechanism. The results show that the proposed mechanism appears to scale well and provides near‐optimal allocations. [ABSTRACT FROM AUTHOR]

Social media has revolutionized the manner in which our society is interconnected. While this extensive connectivity offers numerous benefits, it is also accompanied by significant drawbacks, particularly in terms of the proliferation of fake news and the vast dissemination of hate speech. Identifying offensive comments is a critical task for ensuring the safety of users, which is why industry and academia have been working on developing solutions to this problem. Prior research on hate speech detection has predominantly focused on the English language, with few studies devoted to other languages such as Portuguese. This paper introduces the Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR), a high-quality NLP dataset for offensive language detection, which we make publicly available. The dataset contains 6,354 (extendable to 13,538) comments labeled using a fine-grained three-layer annotation schema compatible with datasets in other languages, which allows the training of multilingual/cross-lingual models. The five NLP tasks available in OLID-BR allow the detection of offensive comments, the classification of the types of offenses such as racism, LGBTQphobia, sexism, xenophobia, and so on, the identification of the type and the target of offensive comments, and the extraction of toxic spans of offensive comments. All those tasks can enhance the capabilities of content moderation systems by providing deep contextual analysis or highlighting the spans that make a text toxic. We further experiment with and evaluate the dataset using state-of-the-art BERT-based and NER models, which demonstrates the usefulness of OLID-BR for the development of toxicity detection systems for Portuguese texts. [ABSTRACT FROM AUTHOR]

Model-driven engineering provides abstractions and notations to improve the understanding and to support modeling, coding, and verification of applications for specific domains. Ontologies, on the other hand, provide formal and explicit definitions of shared conceptualizations and enable the use of semantic reasoning. Although these areas have been developed by different communities, significant synergy can be achieved when both are combined. These advantages can be explored in the development of multi-agent systems, given their complexity and the need for integrating several components that are often addressed from different angles. This work investigates how to apply ontologies for agent-oriented software engineering. Initially, we present a new modeling approach where multi-agent systems are designed by instantiating our proposed ontology. An additional contribution is a tool that uses instantiated ontological designs to generate programming code for such systems. Several advantages can be obtained from the application of our ontology-based approach, in terms of specification, development, and verification of agent-oriented software, as indicated by the experiments we have carried out. [ABSTRACT FROM AUTHOR]

Abstract: This paper brings together agent oriented programming, organisation oriented programming and environment oriented programming, all of which are programming paradigms that emerged out of research in the area of multi-agent systems. In putting together a programming model and concrete platform called JaCaMo which integrates important results and technologies in all those research directions, we show in this paper, with the combined paradigm, that we prefer to call “multi-agent oriented programming”, the full potential of multi-agent systems as a programming paradigm. JaCaMo builds upon three existing platforms: Jason for programming autonomous agents, for programming agent organisations, and CArtAgO for programming shared environments. This paper also includes a simple example that illustrates the approach and discusses some real-world applications that have been or are being developed with JaCaMo. [Copyright &y& Elsevier]

The concept of an agent is increasingly used in contemporary software applications, particularly those involving the Internet, autonomous systems, or cooperation. However, with dependability and safety in mind, it is vital that the mechanisms for representing and implementing agents are clear and consistent. Hence there has been a strong research effort directed at using formal logic as the basis for agent descriptions and agent implementation. Such a logical basis not only presents the clarity and consistency required but also allows for important techniques such as logical verification to be applied. We present a road map of research into the use of computational logic in agent-based systems and survey much of the recent work in these areas. Even though, with such a rapidly changing field, it is impossible to cover every development, we aim to give the reader sufficient background to understand the current research problems and potential future developments in this maturing area. [ABSTRACT FROM AUTHOR]

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents. [Copyright &y& Elsevier]

The theory of argumentation pervades several fields of knowledge, and it has gained significant space in multiagent systems because it provides a way for modeling reasoning over conflicting information in intelligent agents. This work proposes the development of an argumentation-based inference mechanism for BDI agents based on Toulmin's model of argumentation. The philosopher Stephen Toulmin claimed that arguments typically consist of six parts: data, warrant, claim, backing, qualifier, and rebuttal. This argumentation structure allows arguments to be described through separated components, making it easier to define and to evaluate the inference process. By presenting and discussing some case studies, this paper shows how this mechanism supports the inference of new beliefs based on available evidence within BDI agents programmed in an agent-oriented programming language. [ABSTRACT FROM AUTHOR]

Agreement, cooperation and trust would be straightforward if deception did not ever occur in communicative interactions. Humans have deceived one another since the species began. Do machines deceive one another or indeed humans? If they do, how may we detect this? To detect machine deception, arguably requires a model of how machines may deceive, and how such deception may be identified. Theory of Mind (ToM) provides the opportunity to create intelligent machines that are able to model the minds of other agents. The future implications of a machine that has the capability to understand other minds (human or artificial) and that also has the reasons and intentions to deceive others are dark from an ethical perspective. Being able to understand the dishonest and unethical behaviour of such machines is crucial to current research in AI. In this paper, we present a high-level approach for modelling machine deception using ToM under factors of uncertainty and we propose an implementation of this model in an Agent-Oriented Programming Language (AOPL). We show that the Multi-Agent Systems (MAS) paradigm can be used to integrate concepts from two major theories of deception, namely Information Manipulation Theory 2 (IMT2) and Interpersonal Deception Theory (IDT), and how to apply these concepts in order to build a model of computational deception that takes into account ToM. To show how agents use ToM in order to deceive, we define an epistemic agent mechanism using BDI-like architectures to analyse deceptive interactions between deceivers and their potential targets and we also explain the steps in which the model can be implemented in an AOPL. To the best of our knowledge, this work is one of the first attempts in AI that (i) uses ToM along with components of IMT2 and IDT in order to analyse deceptive interactions and (ii) implements such a model. [ABSTRACT FROM AUTHOR]

The AGERE! workshop 1 has been organized with the ACM SPLASH conference since 2011. The workshop has brought together researchers in programming systems, languages, and applications based on actors, active/concurrent objects, agents, and more broadly, high-level programming paradigms which facilitate decentralized control. The goal of the workshop is to promote use of such paradigms to address the problem of developing software for complex, real-world applications. The AGERE workshop is a follow on to workshops on Object-based Concurrent Programming which were organized twenty years earlier in conjunction with OOPSLA [1–3] . In the last two decades, concurrency and distribution have become part of everyday programming. In this context, the objective of AGERE! is to foster the development and adoption of high-level programming paradigms embracing concurrency at the core of the developed abstractions and constructs. This special issue collects extended and enhanced versions of selected papers from AGERE! 2011 and 2012 which underwent additional review cycles. [ABSTRACT FROM AUTHOR]

The Multi-Agent Programming Contest in 2017 expanded upon the Agents in the City scenario used in the 2016 edition of the contest. In the Agents in the City contest, two teams compete to accomplish logistic tasks in simulations using realistic city maps from OpenStreetMap. The new version of the scenario shifted emphasis to include jobs that require assembled items, new types of facilities, and larger teams, resulting in a significantly more complex scenario than before. In this paper, we describe the strategies used by our team, highlighting our adaptations and any new additions from our participation in the previous year. One such new addition, is that now we have fully explored the use of all three programming dimensions (agent, environment, and organization) found in JaCaMo, the multi-agent system development platform that we used to implement our team. We also provide some discussion and analysis on what we believe were some of our most influential matches. [ABSTRACT FROM AUTHOR]

Several advantages can be obtained by allowing multi-agent systems to easily access ontologies, for example, in scenarios where agents make their decisions based on knowledge provided by ontologies. Thus, this paper presents an infrastructure to allow the use of web ontologies in different agent-oriented platforms. The agents use this infrastructure layer as a tool for storing, accessing and querying domain-specific OWL ontologies. As a result, this layer allows an integration of agent platforms with semantic web data and ontologies. We exemplify in practice how agents, coded in one such platform, can use the proposed access layer to ontological reasoning engines, as well as which features can be obtained from it. We evaluated and compared performance and memory consumption of this semantic infrastructure against usual knowledge representation in agent programming. [ABSTRACT FROM AUTHOR]

In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for an ontologically relevant plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the subsumption relation between concepts, but also on ontology matching. Belief querying and updating also take advantage of ontological reasoning and matching. [ABSTRACT FROM AUTHOR]

Production and characterization of polymeric nanoparticles, as colloidal dispersions, are processes that require time and technical skills to make the results accurate. Computational simulations in nanoscience have been used to help in these processes and provide agility and support to reach results: stability and quality in dispersions. Multi-Agent System for Polymeric Nanoparticles (MASPN) is an innovative and original simulation environment with features to demonstrate interactions of particles from physical-chemical parameters, ensuring Brownian motion of particles and attractive and repulsive behaviour. The MASPN environment has been designed and has been built according to the feature-driven development (FDD), as software methodology, and a multi-agent systems approach. In addition, we have used the event-driven simulation package algs4, the JASON agent building environment, all integrated by Java language. This paper aims to present the relation of the algs4 package and the JASON tool, both integrated into the MASPN environment to generate Brownian motion with elastic and inelastic collisions. The MASPN environment as a simulation tool emerges as a result, including the following features: graphical interface; integrated physical-chemical parameters; Brownian motion; JASON and algs4 integration; and distribution charts (size, zeta potential, and pH). [ABSTRACT FROM AUTHOR]