Towards Jacamo-rest: A Resource-Oriented Abstraction for Managing Multi-Agent Systems

The Multi-Agent Oriented Programming (MAOP) paradigm provides abstractions to model and implements entities of agents, as well as of their organisations and environments. In recent years, researchers have started to explore the integration of MAOP and the resource-oriented web architecture (REST). This paper further advances this line of research by presenting an ongoing work on jacamo-rest, a resource-oriented web-based abstraction for the multi-agent programming platform JaCaMo. Jacamo-rest takes Multi-Agent System (MAS) interoperability to a new level, enabling MAS to not only interact with services or applications of the World Wide Web but also to be managed and updated in their specifications by other applications. To add a developer interface to JaCaMo that is suitable for the Web, we provide a novel conceptual perspective on the management of MAOP specification entities as web resources. We tested jacamo-rest using it as a middleware of a programming interface application that provides modern software engineering facilities such as continuous deployments and iterative software development for MAS.Comment: 11 pages, 5 figures, Accepted to present on 14th Workshop-School on Agents, Environments, and Applications (WESAAC 2020

Engineering Multiagent Systems - Reflections

This report documents the programme and outcomes of Dagstuhl Seminar 12342 ``Engineering multiagent Systems\u27\u27. The seminar brought together researchers from both academia and industry to identify the potential for and facilitate convergence towards standards for agent technology. As such it was particularly relevant to industrial research. A key objective of the seminar, moreover, has been to establish a road map for engineering multiagent systems. Various research areas have been identified as important topics for a research agenda with a focus on the development of multiagent systems. Among others, these include the integration of agent technology and legacy systems, component-based agent design, standards for tooling, establishing benchmarks for agent technology, and the development of frameworks for coordination and organisation of multiagent systems. This report presents a more detailed discussion of these and other research challenges that were identified. The unique atmosphere of Dagstuhl provided the perfect environment for leading researchers from a wide variety of backgrounds to discuss future directions in programming languages, tools and platforms for multiagent systems, and the road map produced by the seminar will have a timely and decisive impact on the future of this whole area of research

Governance of Autonomous Agents on the Web: Challenges and Opportunities

International audienceThe study of autonomous agents has a long tradition in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this paper motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities

Intentional dialogues in multi-agent systems based on ontologies and argumentation

Some areas of application, for example, healthcare, are known to resist the replacement of human operators by fully autonomous systems. It is typically not transparent to users how artificial intelligence systems make decisions or obtain information, making it difficult for users to trust them. To address this issue, we investigate how argumentation theory and ontology techniques can be used together with reasoning about intentions to build complex natural language dialogues to support human decision-making. Based on such an investigation, we propose MAIDS, a framework for developing multi-agent intentional dialogue systems, which can be used in different domains. Our framework is modular so that it can be used in its entirety or just the modules that fulfil the requirements of each system to be developed. Our work also includes the formalisation of a novel dialogue-subdialogue structure with which we can address ontological or theory-of-mind issues and later return to the main subject. As a case study, we have developed a multi-agent system using the MAIDS framework to support healthcare professionals in making decisions on hospital bed allocations. Furthermore, we evaluated this multi-agent system with domain experts using real data from a hospital. The specialists who evaluated our system strongly agree or agree that the dialogues in which they participated fulfil Cohen’s desiderata for task-oriented dialogue systems. Our agents have the ability to explain to the user how they arrived at certain conclusions. Moreover, they have semantic representations as well as representations of the mental state of the dialogue participants, allowing the formulation of coherent justifications expressed in natural language, therefore, easy for human participants to understand. This indicates the potential of the framework introduced in this thesis for the practical development of explainable intelligent systems as well as systems supporting hybrid intelligence

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

Formal Verification of a Map Merging Protocol in the Multi-Agent Programming Contest

Communication is a critical part of enabling multi-agent systems to cooperate. This means that applying formal methods to protocols governing communication within multi-agent systems provides useful confidence in its reliability. In this paper, we describe the formal verification of a complex communication protocol that coordinates agents merging maps of their environment. The protocol was used by the LFC team in the 2019 edition of the Multi-Agent Programming Contest (MAPC). Our specification of the protocol is written in Communicating Sequential Processes (CSP), which is a well-suited approach to specifying agent communication protocols due to its focus on concurrent communicating systems. We validate the specification's behaviour using scenarios where the correct behaviour is known, and verify that eventually all the maps have merged