New Zealand

Poullin Dominique (February 1st 2010). Passive Radar using COFDM (DAB or DVB-T) Broadcasters as Opportunistic Illuminators, Digital Video, Floriano De Rango, IntechOpen, DOI: 10.5772/8034.

Martin Fleury, Emmanuel Jammeh, Rouzbeh Razavi, Sandro Moiron and Mohammed Ghanbari (March 1st 2010). Video Streaming in Evolving Networks under Fuzzy Logic Control, Trends in Telecommunications Technologies, Christos J Bouras, IntechOpen, DOI: 10.5772/8490.

Ondřej Badura, Tomáš Wroblowský and Jin Han (February 1st 2017). An Influence of Relative Income on the Marginal Propensity to Consume: Evidence from Shanghai, Proceedings of the 2nd Czech-China Scientific Conference 2016, Jaromir Gottvald and Petr Praus, IntechOpen, DOI: 10.5772/66785. 

Opportunism is a behavior that takes advantage of knowledge asymmetry and results in promoting agents&#39; own value and demoting others&#39; value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents&#39; opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. In particular, agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents&#39; state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism.


AAMAS 2020

A Formal Framework for Reasoning about Opportunistic Propensity in Multi-agent Systems

decision theory

propensity

opportunism

reasoning

logic

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Opportunism is a behavior that takes advantage of knowledge asymmetry and results in promoting agents' own value and demoting others' value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents' opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. In particular, agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents' state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism.


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). 

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. 


Sequential Voting in Multi-agent Soft Constraint Aggregation

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. 


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