United Kingdom

Community Health Workers (CHWs) form an important component of health-care systems globally, especially in low-resource settings. CHWs are often tasked with monitoring the health of and intervening on their patient cohort. Previous work has developed several classes of Restless Multi-Armed Bandits (RMABs) that are computationally tractable and indexable, a condition that guarantees asymptotic optimality, for solving such health monitoring and intervention problems (HMIPs). However, existing solutions to HMIPs fail to account for risk-sensitivity considerations of CHWs in the planning stage and may run the danger of ignoring some patients completely because they are deemed less valuable to intervene on. Additionally, these also rely on patients reporting their state of adherence accurately when intervened upon. Towards tackling these issues, our contributions in this paper are as follows: (1) We develop an RMAB solution to HMIPs that allows for reward functions that are monotone increasing, rather than linear, in the belief state and also supports a wider class of observations. (2) We prove theoretical guarantees on the asymptotic optimality of our algorithm for any arbitrary reward function. Additionally, we show that for the specific reward function considered in previous work, our theoretical conditions are stronger than the state-of-the-art guarantees. (3) We show the applicability of these new results for addressing the three issues pertaining to: risk-sensitive planning, equitable allocation and reliance on perfect observations as highlighted above. We evaluate these techniques on both simulated as well as real data from a prevalent CHW task of monitoring adherence of tuberculosis patients to their prescribed medication in Mumbai, India and show improved performance over the state-of-the-art. The simulation code is available at: https://github.com/AdityaMate/risk-aware-bandits.

AAMAS 2021

Risk-Aware Interventions in Public Health: Planning with Restless Multi-Armed Bandits

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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The area of computational social choice (COMSOC) analyzes collective decision problems from an algorithmic perspective. So far, the main focus in this area lied on analyzing problems where a single preference relation for each agent is given and a single solution reflecting all agents' preferences needs to be found. However, this modeling is often not rich enough to capture the changing and ambivalent nature of real-world problems. We will argue that one possibility to incorporate such aspects is to allow for multiple preference profiles in the input and multiple solutions in the output. We systematically review different types of arising settings, point out how classical problems and solution concepts can be generalized, and identify several research challenges.

Broadening the Research Agenda for Computational Social Choice: Multiple Preference Profiles and Multiple Solutions (Blue Sky Ideas Track)

Given their installation on nearly a billion consumer devices around the world, consumers clearly enjoy using voice-driven assistants such as Alexa, Siri and Google Home, and find it compelling to interact with AI agents as quasi-human entities. Inevitably, people who are accustomed to using voice-driven assistants at home and in the car will expect to use such technologies in the workplace. What form will this take? Simple extrapolations from consumer space (e.g. running meetings or presentations) promise only modest value. I propose that AAMAS and the AI research community should pursue a bolder vision in which software agents act as quasi-human collaborators on core business intelligence tasks that entail analyzing data, diagnosing problems, and making decisions. Moreover, these business intelligence agents should communicate multi-modally, i.e. they must understand and employ speech complemented by non-verbal behaviors such as pointing, eye gaze, and facial expression. I outline requisite agent, MAS and AI technologies and pose several fundamental research challenges raised by this vision.

Multi-modal Agents for Business Intelligence (Blue Sky Ideas Track)

Machine learning and artificial intelligence models that interact with and in an environment will unavoidably have impact on this environment and change it. This is often a problem as many methods do not anticipate such a change in the environment and thus may start acting sub-optimally. Although efforts are made to deal with this problem, we believe that a lot of potential is unused. Driven by the recent success of predictive machine learning, we believe that in many scenarios one can predict when and how a change in the environment will occur. In this paper we introduce a blueprint that intimately connects this idea to the multiagent setting, showing that the multiagent community has a pivotal role to play in addressing the challenging problem of changing environments.

Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity? (Blue Sky Ideas Track)

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.

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

To develop and effectively deploy Trustworthy Autonomous Systems (TAS), we face various social, technological, legal, and ethical challenges in which different notions of responsibility can play a key role. In this work, we elaborate on these challenges, discuss research gaps, and show how the multidimensional notion of responsibility can play a role to bridge them. We argue that TAS requires operational tools to represent and reason about responsibilities of humans as well as AI agents. We review major challenges to which responsibility reasoning can contribute, highlight open research problems, and argue for the application of multiagent responsibility models in a variety of TAS domains.

Responsibility Research for Trustworthy Autonomous Systems (Blue Sky Ideas Track)

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 argue that the unique strengths of Belief-Desire-Intention (BDI) agent programming languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems.

Agent Programming in the Cognitive Era (JAAMAS Track)

A relevant application domain for agent-based software is given by smart mobile applications. In this context, the impressive progress of technologies makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures. Accordingly, here we provide a description of JaCa-Android, a framework based on the JaCaMo platform that allows for designing and programming smart mobile apps using BDI-based cognitive agents within the A&A conceptual model.

Programming Agent-based Mobile Apps: The JaCa-Android Framework (JAAMAS Track)

Multiagent system models do not typically encompass tools for tackling abnormal or exceptional situations. When a critical situation arises, and individual agents fail, the system as a whole usually fails, too. For realizing robust distributed systems, conceived as agent systems or organizations, it is necessary to keep a right level of situational awareness (of both agents and environment), through the introduction of the means for gathering and propagating feedback, upon which actions can be taken. This work proposes a notion of accountability that encompasses both a normative dimension, and a structural dimension, that serves for the purpose of robustness.

Robustness Based on Accountability in Multiagent Organizations

Reliable and secure communication between heterogeneously resourced autonomous agents controlled by competing stakeholders in a decentralized environment is a challenge. Agents require a means to find each other and communicate without reliance on a centralized party and participation in the system must be permissionless. We present the Agent Communication Network (ACN), a peer-to-peer lookup system that provides a distributed overlay to the Internet and addresses this problem. The ACN enables agents to find each other and to communicate safely. It achieves this by leveraging a distributed hash table for agent lookup, maintained by participating peers and through the use of public-key cryptography. The paper discusses the properties of the system and its guarantees as well as its integration with a novel multi-agent system. Preliminary benchmark results demonstrate the feasibility of the system, its performance, and its scalability.

Peer-to-peer Autonomous Agent Communication Network

In multi-agent systems the main process responsible for obtaining information about the environment is perception, generally this process is performed passively regardless the agent's intentional state. However, especially when inserted in the real world, a frequent problem is that agents have partial perception of the environment, failing to perceive some relevant information. To circumvent this problem, a solution is to actively take actions to perceive what is of interest to the agent, for example, in a computer vision system, the camera can be repositioned to have a better view of an object. This work aims to develop an active perception model integrated with the reasoning cycle of BDI agents. Experiments are performed using BDI agents with ROS to command unmanned aerial vehicles to analyze the benefits and impacts of using cognitive agents with active perception to program robot intelligence.

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