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Javier B. Cabrera, Manuel F. Veiga, Diego X. Morales and Ricardo Medina (November 27th 2019). Reducing Power Losses in Smart Grids with Cooperative Game Theory, Advanced Communication and Control Methods for Future Smartgrids, Taha Selim Ustun, IntechOpen, DOI: 10.5772/intechopen.88568.

Jing Chen and Junying Jia (March 29th 2020). Automatic Recognition of Tea Diseases Based on Deep Learning [Online First], IntechOpen, DOI: 10.5772/intechopen.91953.

elia Escamilla-Rivera (May 1st 2020). Bayesian Deep Learning for Dark Energy [Online First], IntechOpen, DOI: 10.5772/intechopen.91466. 

AAMAS 2020

An Agent-Based Model for Trajectory Modelling in Shared Spaces: A Combination of Expert Based and Deep Learning Approaches

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). 

technical paper

Online social networks lack support for the collaborative management of access control. This is crucial for content that may involve multiple users such as photos, as this lack of support causes conflicts that lead to privacy violations. Previous research proposed collaborative mechanisms to support users in these cases, but most of these attempts fail to satisfy some desirable requirements, such as explainability, role-agnosticism, adaptability, and being utility- and value-driven at the same time. In this paper, we outline an agent architecture that has been designed to meet all these requirements.


Towards a Value-driven Explainable Agent for Collective Privacy

The booming sharing economy has a common challenge in motivating agents, by truthful actions, to follow the economic design of the market maker. For the well known BitTorrent network, there has been an array of theoretical research in the study of the truthfulness of the proportional response protocol formulated to model its tit-for-tat strategy. Some works confirm the truthfulness for deviations against the false name attack, weight cheating, and edge deleting. Others reveal it not truthful against Sybil attack where an agent may split its resource among its different copies. Though truthfulness cannot be guaranteed, the incentives are shown to generate a limited gain for agents pursuing such an attack in several special networks, such as trees, cliques, and cycles. In this work, we establish the first such results for general networks, proving a \theta(1) incentive ratio for any agent playing such an attack.


Limiting the Deviation Incentives in Resource Sharing Networks

Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models. Among these models include deep reinforcement learning agents. The existing methods for attacking reinforcement learning agents assume the adversary either has access to the target agent's learned parameters or the environment that the agent interacts with. In this work, we propose a new class of threat models, called snooping threat models, that are unique to reinforcement learning. In these snooping threat models, the adversary does not have the ability to interact with the target agent's environment, and can only eavesdrop on the action and reward signals being exchanged between agent and environment. We show that adversaries operating in these highly constrained threat models can still launch devastating attacks against the target agent by training proxy models on related tasks and leveraging the transferability of adversarial examples.


Snooping Attacks on Deep Reinforcement Learning

An interaction protocol specifies a decentralized multiagent system operationally by specifying constraints on messages exchanged by its member agents.  Engineering with protocols requires support for a notion of refinement, whereby a protocol may be substituted without loss of correctness by one that refines it.  We identify two desiderata for refinement. One, generality: refinement should not restrict enactments by limiting protocols or infrastructures under consideration.  Two, preservation: to facilitate modular verification, refinement should preserve liveness and safety.

  We contribute a novel formal notion of protocol refinement based on enactments. We demonstrate generality by tackling the declarative framework of information protocols.  We demonstrate preservation by formally establishing that our notion of refinement is safety and liveness preserving. We show the practical benefits of refinement by implementing a checker. We demonstrate that it is less time-intensive to check refinement (and thereby gain safety and liveness) than to recheck safety and liveness of a composition.


Refinement for Multiagent Protocols

We propose a formalism to model and reason about multi-agent systems. We allow agents to interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. The formalism defines a local behaviour based on shared variables and a global one based on message passing. We extend LTL to be able to reason explicitly about the intentions of the different agents and their interaction protocols. We also study the complexity of satisfiability and model-checking of this extension.


Reconfigurable Interaction for MAS Modelling

In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer). We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pre-match decisions and a stochastic game to model the in-match state transitions and decisions. Using this formulation, we propose a method to predict the probability of game outcomes and the payoffs of team actions. Building upon this, we develop algorithms to optimise team formation and in-game tactics with different objectives. Empirical evaluation of our approach on real-world datasets from 760 matches shows that by using optimised tactics from our Bayesian and stochastic games, we increase a team chances of winning by 16.1% and 3.4% respectively. 


Optimising Game Tactics for Football

We propose a market-inspired bidding scheme for the assignment of paper reviews in large academic conferences. The primary contribution is an analysis of the incentives of reviewers during the bidding phases, when reviewers have private costs and some information about the demand for each paper, and want to obtain the best possible k papers for a predetermined k. We show that by assigning budgets to reviewers and a `price' for every paper that is (roughly) inversely proportional to its demand, the best response of a reviewer is to bid sincerely, i.e., on her most favorite papers, and match the budget even when it is not enforced. Finally, we show via extensive simulations on bidding data from real conferences, that our suggested bidding scheme would substantially improve the assignment, under several common assignment algorithms.

A Market-Inspired Bidding Scheme for Peer Review Paper Assignment

Equitable Allocations of Invisible Chores

Reinforcement Learning (RL) has been widely used to solve sequential decision-making problems. However, RL algorithms suffer from poor sample efficiency and require a long time to learn a suitable policy, especially when multiple agents are learning without prior knowledge. This problem can be alleviated through reusing knowledge from other agents during the learning process. One notable approach is advising actions based on a teacher-student relationship, where the decision of a student agent during learning is aided by an experienced teacher agent. A critical assumption in teacher-student paradigm is that the communication may be limited, so that a student may wait for a while and learn by itself before receiving the next advice. More importantly, in some noisy or stochastic environments, the student may not be able to mas- ter the advised actions when they are only performed once. We propose three methods for agents choosing between learning by exploration, asking for advice and reusing previous advice. The results show that our approaches significantly outperform existing advising methods without reusing advice.


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Towards a Value-driven Explainable Agent for Collective Privacy

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Trajectory Modelling in Shared Spaces: Expert-Based vs. Deep Learning Approach?

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Next from AAMAS 2020

Towards a Value-driven Explainable Agent for Collective Privacy

Similar lecture

Trajectory Modelling in Shared Spaces: Expert-Based vs. Deep Learning Approach?

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