AI Communications最新文献_第6页

Conversational AI for multi-agent communication in Natural Language 自然语言中多智能体通信的会话AI

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-15 DOI: 10.3233/aic-220147

Oliver Lemon

引用次数: 2

Multi-agent systems research in the United Kingdom 英国的多智能体系统研究

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-14 DOI: 10.3233/aic-229003

Stefano V. Albrecht, M. Wooldridge

{"title":"Multi-agent systems research in the United Kingdom","authors":"Stefano V. Albrecht, M. Wooldridge","doi":"10.3233/aic-229003","DOIUrl":"https://doi.org/10.3233/aic-229003","url":null,"abstract":"Multi-agent systems have been a core research topic in artiﬁcial intelligence for several decades. A multi-agent system consists of multiple decision-making agents – which may be software-based AI systems, physically-embodied robots, or humans – which must interact in a shared environment in pursuit of their goals. Multi-agent systems research spans a range of technical problems, such as how to design planning and learning algorithms which enable agents to achieve their goals; how to design multi-agent systems to incentivise certain behaviours in agents; how information is communicated and propagated among agents; and how norms, conventions, and roles may emerge in multi-agent systems. A vast array of applications have been addressed using multi-agent method-ologies, including autonomous driving, multi-robot factories, automated trading, commercial games, automated tutoring, and robotic rescue teams. The purpose of this special issue is to showcase current multi-agent systems research led by university and industry groups based in the United Kingdom. Research groups and institutes in the UK which have signiﬁcant activity in multi-agent systems research were invited to submit an article describing: (1) the technical problems in multi-agent systems tackled by the group (their core research agenda), including applications and industry collaboration; (2) the main approaches developed by the group and any key results achieved; and (3) important open challenges in multi-agent systems research from the perspective of the group. A large number of high-quality submissions were received, of which 14 were included for publication in the special issue. These articles represent a broad set of research topics within the ﬁeld of multi-agent systems, showcasing the strength of contributions made by UK-based research groups in both universities and industry. We believe the open research problems discussed in each of the articles will provide a rich resource for researchers in this ﬁeld, both new and old.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"1243 1","pages":"269-270"},"PeriodicalIF":0.8,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76812686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Interaction-Oriented Software Engineering: Programming abstractions for autonomy and decentralization 面向交互的软件工程:自治和去中心化的编程抽象

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-14 DOI: 10.3233/aic-220144

A. Chopra

引用次数: 2

Reasoning and interaction for social artificial intelligence 社会性人工智能的推理与交互

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-12 DOI: 10.3233/aic-220133

Elizabeth Black, M. Brandão, O. Cocarascu, Bart de Keijzer, Yali Du, Derek Long, Michael Luck, P. McBurney, Albert Meroño-Peñuela, S. Miles, S. Modgil, L. Moreau, M. Polukarov, O. Rodrigues, Carmine Ventre

{"title":"Reasoning and interaction for social artificial intelligence","authors":"Elizabeth Black, M. Brandão, O. Cocarascu, Bart de Keijzer, Yali Du, Derek Long, Michael Luck, P. McBurney, Albert Meroño-Peñuela, S. Miles, S. Modgil, L. Moreau, M. Polukarov, O. Rodrigues, Carmine Ventre","doi":"10.3233/aic-220133","DOIUrl":"https://doi.org/10.3233/aic-220133","url":null,"abstract":"Current work on multi-agent systems at King’s College London is extensive, though largely based in two research groups within the Department of Informatics: the Distributed Artificial Intelligence (DAI) thematic group and the Reasoning & Planning (RAP) thematic group. DAI combines AI expertise with political and economic theories and data, to explore social and technological contexts of interacting intelligent entities. It develops computational models for analysing social, political and economic phenomena to improve the effectiveness and fairness of policies and regulations, and combines intelligent agent systems, software engineering, norms, trust and reputation, agent-based simulation, communication and provenance of data, knowledge engineering, crowd computing and semantic technologies, and algorithmic game theory and computational social choice, to address problems arising in autonomous systems, financial markets, privacy and security, urban living and health. RAP conducts research in symbolic models for reasoning involving argumentation, knowledge representation, planning, and other related areas, including development of logical models of argumentation-based reasoning and decision-making, and their usage for explainable AI and integration of machine and human reasoning, as well as combining planning and argumentation methodologies for strategic argumentation.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"91 1","pages":"309-325"},"PeriodicalIF":0.8,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72737843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Verifiable autonomy: From theory to applications 可验证自主性:从理论到应用

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-09 DOI: 10.3233/aic-220115

Louise Dennis, C. Dixon, M. Fisher

引用次数: 1

Agent-based modelling for Urban Analytics: State of the art and challenges 基于主体的城市分析模型:现状与挑战

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-06 DOI: 10.3233/AIC-220114

N. Malleson, M. Birkin, Daniel Birks, Jiaqi Ge, A. Heppenstall, E. Manley, J. McCulloch, Patricia Ternes

{"title":"Agent-based modelling for Urban Analytics: State of the art and challenges","authors":"N. Malleson, M. Birkin, Daniel Birks, Jiaqi Ge, A. Heppenstall, E. Manley, J. McCulloch, Patricia Ternes","doi":"10.3233/AIC-220114","DOIUrl":"https://doi.org/10.3233/AIC-220114","url":null,"abstract":"Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual ‘agents’, and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"64 1","pages":"393-406"},"PeriodicalIF":0.8,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74543864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Dependable learning-enabled multiagent systems 可靠的支持学习的多智能体系统

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-06 DOI: 10.3233/aic-220128

Xiaowei Huang, Bei Peng, Xingyu Zhao

{"title":"Dependable learning-enabled multiagent systems","authors":"Xiaowei Huang, Bei Peng, Xingyu Zhao","doi":"10.3233/aic-220128","DOIUrl":"https://doi.org/10.3233/aic-220128","url":null,"abstract":"We are concerned with the construction, formal verification, and safety assurance of dependable multiagent systems. For the case where the system (agents and their environment) can be explicitly modelled, we develop formal verification methods over several logic languages, such as temporal epistemic logic and strategy logic, to reason about the knowledge and strategy of the agents. For the case where the system cannot be explicitly modelled, we study multiagent deep reinforcement learning, aiming to develop efficient and scalable learning methods for cooperative multiagent tasks. In addition to these, we develop (both formal and simulation-based) verification methods for the neural network based perception agent that is trained with supervised learning, considering its safety and robustness against attacks from an adversarial agent, and other approaches (such as explainable AI, reliability assessment, and safety argument) for the analysis and assurance of the learning components. Our ultimate objective is to combine formal methods, machine learning, and reliability engineering to not only develop dependable learning-enabled multiagent systems but also provide rigorous methods for the verification and assurance of such systems.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"29 1","pages":"407-420"},"PeriodicalIF":0.8,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78210485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Resilience, reliability, and coordination in autonomous multi-agent systems 自主多主体系统中的弹性、可靠性和协调性

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-06 DOI: 10.3233/aic-220136

R. C. Cardoso, B. Logan, Felipe Meneguzzi, N. Oren, Bruno Yun

引用次数: 2

Perspectives on the system-level design of a safe autonomous driving stack 安全自动驾驶堆栈系统级设计展望

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-02 DOI: 10.3233/aic-220148

Majd Hawasly, Jonathan Sadeghi, Morris Antonello, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy

引用次数: 0

Deep reinforcement learning for multi-agent interaction 多智能体交互的深度强化学习

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-01 DOI: 10.3233/aic-220116

Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht

引用次数: 0