United Kingdom

We identify several challenges and opportunities opened to agent and multiagent systems, following the recent developments in the domain of Earth observation constellations. We focus on three challenge categories that manifest in this field: (i) configuration problems of constellations and ground stations used to operate them, potentially owned by different actors, as to provide better services and coordination; (ii) offline planning and scheduling problems, which consist in finding solution methods to schedule observation and upload/download tasks over the constellation; (iii) the design of efficient and reactive online operation methods as to adapt schedules in dynamic settings. Being naturally distributed and composed of multiple entities and users, these problems clearly fit the multiagent paradigm, and may challenge researchers for many years.

AAMAS 2021

Autonomous Agents and Multiagent Systems Challenges in Earth Observation Satellite Constellations (Blue Sky Ideas Track)

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

technical paper

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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A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study.

Sparse Training Theory for Scalable and Efficient Agents (Blue Sky Ideas Track)

This paper presents objective metrics for how explainable artificial intelligence (XAI) can be quantified. Through an overview of current trends, we show that many explanations are generated post-hoc and independent of the agent's logical process, which in turn creates explanations with limited meaning as they lack transparency and fidelity. While user studies are a known basis for evaluating XAI, studies that do not consider objective metrics for evaluating XAI may have limited meaning and may suffer from confirmation bias, particularly if they use low fidelity explanations unnecessarily. To avoid this issue, this paper suggests a paradigm shift in evaluating XAI that focuses on metrics that quantify the explanation itself and its appropriateness given the XAI goal. We suggest four such metrics based on performance differences, D, between the explanation's logic and the agent's actual performance, the number of rules, R, outputted by the explanation, the number of features, F, used to generate that explanation, and the stability, S, of the explanation. We believe that user studies that focus on these metrics in their evaluations are inherently more valid and should be integrated in future XAI research.

Better Metrics for Evaluating Explainable Artificial Intelligence (Blue Sky Ideas Track)

The demand for intelligent virtual advisors in our rapidly advancing world is rising and, consequently, the need for understanding the reasoning process to answer why a particular piece of advice is provided to the user is directly increasing. Personalized explanation is regarded as a reliable way to improve the user's understanding and trust in the virtual advisor. So far, cognitive explainable agents utilize reason explanation by referring to their own mental state (beliefs and goals) to explain their own behaviour. However, when the explainable agent plays the role of a virtual advisor and recommends a behaviour for the human to perform, it is best to refer to the user's mental state, rather than the agent’s mental state, to form a reason explanation. In this paper, we are developing an explainable virtual advisor (XVA) that communicates with the user to elicit the user's beliefs and goals and then tailors its advice and explains it according to the user's mental state. We tested the proposed XVA with university students where the XVA provides tips to reduce the students' study stress. We measured the impact of receiving three different patterns of tailored explanations (belief-based, goal-based, and belief&goal-based explanation) in terms of the students' intentions to change their behaviours. The results showed that the intention to change is not only related to the explanation pattern but also to the user context, the relationship built with the agent, the type of behaviour recommended and the user’s current intention to do the behaviour.

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Next from AAMAS 2021

Sparse Training Theory for Scalable and Efficient Agents (Blue Sky Ideas Track)

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Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions

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