Khair Eddin Sabri, Ridha Khedri and Jason Jaskolka (October 1st 2009). Algebraic Model for Agent Explicit Knowledge in Multi-agent Systems, Advanced Technologies, Kankesu Jayanthakumaran, IntechOpen, DOI: 10.5772/8211. 

Carl Frelicot and Hoel Le Capitaine (February 1st 2010). Class-Selective Rejection Rules Based on the Aggregation of Pattern Soft Labels, Pattern Recognition Recent Advances, Adam Herout, IntechOpen, DOI: 10.5772/9361. 

New Zealand

We study a sequential preference aggregation procedure based on voting rules for settings where several agents express their preferences over a common set of variable assignments via soft constraints. We evaluate this approach by providing both theoretical and experimental results. 


AAMAS 2020

Sequential Voting in Multi-agent Soft Constraint Aggregation

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Many real-world decision problems are inherently multi-objective in nature and concern multiple actors, making multi-objective multi-agent systems a key domain to study. We argue that trade-offs between conflicting objective functions should be analysed on the basis of the utility that these trade-offs have for the users of a system. We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures and utility functions. We analyse which solution concepts apply to the different settings in our taxonomy, which allows us to offer a structured view of the field and identify promising directions for future research.


Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

Multi-objective multi-agent decision making: a utility based analysis and survey

The literature on norm emergence and normative MAS considers norms from two perspectives, namely: prescriptive norms using deontic concepts, and emergent norms that capture preference behaviour. We find that both perspectives lend themselves naturally to specific ways of representing norms in a society, as either explicit or implicit. 
Our analysis of the norm emergence literature, contributes several insights for future research in normative MAS. For example, opportunities for the study of online norm synthesis mechanisms and the investigation of the conversion of social norms, typically observed in norm emergence, to legal norms in normative MAS.  
Conversely, concepts from normative MAS can be brought into norm emergence research. For example, the study of stable emergence to avoid instability in MAS and the investigation of whether high cognitive ability agents, typically found in normative MAS, can demonstrate norm emergence. 
We summarise our analysis and outline future challenges and opportunities for cross-over between norm-emergence and normative systems research.


Norm Emergence in Multiagent Systems: A Viewpoint Paper

This presentation presents the Multi-Agent based Real-Time INfrastructure for Energy (MARTINE). MARTINE is a simulation and emulation infrastructure for the study of power and energy systems using a combination of artificial intelligence approaches. MARTINE combines real buildings with sensoring and actuation capabilities with real-time simulation, emulation of physical resources and the intelligent decision support to players actions. The infrastructure is managed and operated by means of several multi-agent systems, which connect to physical resources but also represent additional simulated players that are not present physically in the simulation and emulation environment.


MARTINE: Multi-Agent based Real-Time INfrastructure for Energy

When robots act in an environment, there will be temporal uncertainty over the execution of their actions, i.e. the duration of an action and the time it takes place will be stochastic. The presence of multiple robots in the environment contributes towards this uncertainty.  Temporal uncertainty is often disregarded in multi-robot coordination, and so in this presentation, I will discuss why it is important to model temporal uncertainty, and detail the benefits of doing so. Following this, I will outline my current work modelling temporal uncertainty in terms of congestion in order to generate effective plans for multi-robot navigation tasks. Finally, I will discuss the requirements to achieve general multi-robot coordination under temporal uncertainty.


Towards Multi-Robot Coordination under Temporal Uncertainty (Doctoral Consortium)

This paper presents part of the work developed so far within the scope of my PhD and suggests possible future research directions. My thesis tackles the problem of multi-robot coordination under uncertainty over the long-term. We present a preliminary approach that tackles multi-robot monitoring problems under uncertain task durations. We propose a methodology that takes advantage of a modeling formalism for robot teams: generalized stochastic Petri nets with rewards (GSPNR). A GSPNR allows for unified modeling of action selection and uncertainty on duration of action execution. At the same time, it allows for goal specification through the use of transition rewards and rewards per time unit. The proposed approach exploits the well-defined semantics provided by Markov reward automata in order to synthesize policies.


Long-Run Multi-Robot Planning Under Uncertain Task Durations

Coalition formation games aim at predicting the cooperative behavior of agents when forming alliances. Agents entertain preferences over coalition structures, and the goal is to find a coalition structure that is good for both individual agents and the society as an entity. We measure the quality of partitions in terms of Pareto optimality, individual rationality, and various notions of popularity. We give both efficient algorithms and hardness results for computing partitions that satisfy these properties for additively separable hedonic games and give an outlook to results on other classes of coalition formation games, including roommate games and flatmate games.

Desirable Partitions in Coalition Formation Games

The advent of data marketplaces and the increasing awareness of data privacy call for data privacy pricing mechanisms which can set prices of data properly while assuring privacy preservation. Moreover, the trade-off between the privacy of data and the accuracy of results need to be considered. Additionally, data marketplaces have different structures, including buy-sided market, sell-sided market, two-sided market, and two-sided platform. I will explore data privacy pricing mechanisms under these four structures.


Incentive Mechanisms for Data Privacy Preservation and Pricing

In Boolean games, each agent controls a set of Boolean variables and has a
goal represented by a propositional formula. We initiate a study of inference in Boolean games assuming the presence of a principle who has the
ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We show how taxation schemes can also be used to infer agents' goals. In our formulation, agents' goals are assumed to be unknown and the objective of the principle is to infer the goals of all the agents using appropriate taxation queries. Using an undirected graph (called the goal
overlap graph) associated with a Boolean game, we establish necessary and sufficient conditions for the existence of a Nash equilibrium for any
taxation query. Using these conditions, we develop an algorithm that uses taxation queries to learn agents' goals. Using a valid node coloring of the goal overlap graph, we show that goals of many agents can be inferred simultaneously. We also present more efficient (in terms of number of queries) goal inference algorithms for two special classes of Boolean functions, namely threshold and symmetric functions. 


Boolean Games: Inferring Agents' Goals Using Taxation Queries

We introduce ballooning multi-armed bandits (BL-MAB), a novel extension to the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. The regret in a BL-MAB setting is computed with respect to the best available arm at each time. We first observe that the existing stochastic MAB algorithms are not regret-optimal for the BL-MAB model. We show that if the best arm is equally likely to arrive at any time, a sub-linear regret cannot be achieved, irrespective of the arrival of the other arms. We further show that if the best arm is more likely to arrive in the early rounds, one can achieve sub-linear regret. Making reasonable assumptions on the arrival distribution of the best arm in terms of the thinness of the distribution’s tail, we prove that the proposed algorithm achieves sub-linear instance-independent regret. We further quantify explicit dependence of regret on the arrival distribution parameters.


Sequential Voting in Multi-agent Soft Constraint Aggregation

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Next from AAMAS 2020

Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

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Covariate-Shift Generalization via Random Sample Weighting

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