Spain

The next technological revolution will be interwoven to the proliferation of intelligent systems. As we bridge the gap between physical and cyber worlds, we will give rise to large-scale, multi-agent based technologies. A key challenge that cities of the future will have to face is coordination in the use of limited resources, central to which is finding an optimal allocation between agents. To truly allow for scalable solutions, we need to shift from traditional approaches, to multi-agent solutions, ideally run on-device. We highlight a novel heuristic (ALMA), which exhibits such properties, for solving the assignment problem. ALMA is decentralized, requires only partial feedback, and has constant in the total problem size running time, under reasonable assumptions on the preference domain of the agents, making it ideal for an on-device implementation. We have evaluated ALMA in a variety of scenarios including synthetic and real data, time constraints, and on-line settings. In all of the cases, ALMA was able to reach high social welfare, while being orders of magnitude faster than the centralized, optimal algorithm.

ECAI 2020

Efficient Allocations in Constant Time: Towards Scalable Solutions in the Era of Large Scale Intelligent Systems

on-device ai

assignment problem

maximum weight matching

coordination and cooperation

multi-agent learning

resource allocation

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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A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

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).

poster

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

This paper highlights a new learning model that introduces a contextual dimension to the well-known Q-Learning algorithm. Through the identification of different contexts, the learning process is adapted accordingly, thus converging to enhanced results. The proposed learning model includes a simulated annealing (SA) process that accelerates the convergence process. The model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real electricity market data

Contextual Q-Learning

"How can Artificial Intelligence help a stateless minority from online abuse? Research efforts in hate speech detection thus far have largely focused on identifying and subsequently filtering out negative content that specifically targets them. In this paper, we highlight a recent work that tackles a different aspect of web-vulnerability of marginalized communities: sparsity of pro-minority voices championing their cause. The highlighted paper advocates that blocking hate alone may not be sufficient in these cases as the internet shapes community perception to a great extent in modern times and supportive comments to a vulnerable community serve
 a different purpose. Using an Active Sampling approach, the paper constructs a nuanced voice-for-the-voiceless classifier that automatically discovers comments supporting an (allegedly) persecuted minority. In the context of the Rohingya refugee crisis, one of the biggest humanitarian crises of modern times, the paper presents promising results that can substantially aid content moderation efforts in finding positive content supporting the Rohingyas."

The Refugee Experience Online: Surfacing Positivity Amidst Hate

Modelling a problem as finding a shortest path in a graph has been successful in many AI problems. However, the classical shortest path framework might not be suitable for domains such as planning a career pathway optimized to the skills needed along the way. To address this gap, we introduce graphs with item sets, where each node has a set of items from a finite, global set. We define a cost function for paths in graphs with item sets and show that it lacks the prefix optimality property. We further define two decision problems inspired from career planning. We show that these are NP-complete, unlike the traditional shortest path problem, which is solved in polynomial time. Our study gives a theoretical understanding that could be used as a basis for designing efficient solving approaches to the problems studied.

The Challenge of Optimal Paths in Graphs with Item Sets

As "Big Data" has become pervasive, an increasing amount of research has connected the dots between human behaviour in the offline and online worlds. Consequently, researchers have exploited these new findings to create models that better predict different aspects of human life and recommend future behaviour. To date, however, we do not yet fully understand the similarities and differences of human behaviour in these virtual and physical worlds. Here, we analyse and discuss the mobility and application usage of 400,000 individuals over eight months. We find an astonishing similarity between people's mobility in the physical space and how they move from app to app in smartphones. Our data shows that individuals use and visit a finite number of apps and places, but they keep exploring over time. In particular, two distinct profiles of individuals emerge: those that keep changing places and services, and those that are stable over time, named as "explorers" and "keepers". We see these findings as crucial to enrich a discussion for the potentials and the challenges of building human-centric AI systems, which might leverage recent results in Computational Social Science.

Understanding individual behaviour: from virtual to physical patterns

The prevailing methodology for integrating decision making and ethics is to modify autonomous systems in an ad hoc way to incorporate moral sensibility. However, these provisional modifications often lead to behavior that jeopardizes the intentions of developers or the values of stakeholders. We propose a novel approach for building moral autonomous systems that optimally completes a task and follows an ethical framework by decoupling ethical compliance from task completion. This paper offers a formal definition of our approach along with its key properties, an example based on prima facie duties, and a demonstration that uses our open source library.

An Integrated Approach to Moral Autonomous Systems

Contracts are the cornerstone of legal agreements in the industry. As essential legal documents, contracts are subject to negotiations, demanding extensive analysis and evaluation efforts. Although the emergence of machine learning has enabled assistance tools for text analysis tasks, the specificity and constraints of each
 business context remain obstacles for the automation of contracts evaluation. In this paper, we propose an AutoML based approach for automated negotiation assistance that uses expert annotated contracts and the business-specific knowledge of acceptance policies. Driven by policies rules, our approach generates a classification process composed of hierarchies of complementary classifiers, each being automatically prepared according, but not limited to, feature extraction, learning model and data granularity. Experiments conducted on real-world service contracts have yielded promising results.

Toward a Generic AutoML-Based Assistant for Contracts Negotiation

Malaria is one of the primary causes of death in Mozambique, responsible for an estimated 15,000 deaths in 2017. Human-mediated parasite movement combined with spatial and seasonal changes in transmission threatens the success of malaria interventions by reintroducing parasites to areas targeted for elimination. In this study we used pseudonymized and aggregated call detail records from a sample of over 8.5 million subscribers of a mobile phone operator in Mozambique to quantify human movements for four months in 2017. We calculated parasite importation and exportation rates to identify potential net sinks and sources of malaria across districts. We used a community structure algorithm to explore the connectedness of provinces in Mozambique, and we calculated parasite mobility between urban and rural areas. This work will help provide evidence to inform malaria elimination strategies regionally.

Malaria parasite mobility in Mozambique estimated using mobile phone records

In AI and ML, several methods rely on stochasticity or randomization: mutation and crossover in evolutionary algorithms; dropout and stochastic gradient descent in deep learning; stochasticity in stochastic local search (SLS); and randomization in systematic search. SLS algorithms, which we study here, are competitive in solving computationally hard problems such as satisfiability (SAT), sparse signal recovery, scheduling, and most probable explanations in Bayesian networks (BNs). Essentially, SLS algorithms are greedy optimizers that also make random moves in order to avoid getting trapped in local but non-global optima. 
 
 Clearly, SLS is a well-established idea in AI. However, SLS is sometimes underutilized and there are certain potential difficulties when applying SLS. This highlight showcases recent developments that address some of these difficulties - related to ML, formalization with Markov chains, and setting hyperparameters - and briefly points to future research directions with potential.

Stochastic Local Search and Machine Learning: From Theory to Applications and Vice Versa

In this paper, we identify a series of problems to track and understand what AI is capable of, surveying some previous initiatives. We present the AIcollaboratory, a data-driven framework to collect and explore data about AI results, progress and ultimately capabilities, being developed in the context of AI Watch, the European Commission (EC) knowledge service to monitor the development, uptake and impact of AI in Europe. We close the paper with some challenges for the community emerging around the AIcollaboratory.

Tracking AI: The Capability is (Not) Near

Ontologies are widely used to formally represent abstract domain knowledge. Logic reasoning ensures the logical consistency of ontologies, and infers knowledge implicitly encoded in ontologies. It has been shown both theoretically and empirically that for large and complex ontologies, reasoning is still time-consuming and resource-intensive. Meta-reasoning exploits machine learning techniques to tackle the important problems of understanding the source of reasoning hardness and to predict reasoning efficiency, with the overall goal of improving reasoning efficiency. In this paper, we highlight recent advances in meta-reasoning for Semantic Web ontologies, briefly present technical innovations and results, and discuss important problems for future research.

Efficient Allocations in Constant Time: Towards Scalable Solutions in the Era of Large Scale Intelligent Systems

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Contextual Q-Learning

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