Douglas B. Clark, Paul Medlock-Walton, Raúl Boquín and Eric Klopfer (December 20th 2017). Multiplayer Disciplinarily-Integrated Agent-Based Games: SURGE Gameblox, Simulation and Gaming, Dragan Cvetković, IntechOpen, DOI: 10.5772/intechopen.73051.

Nadine Barrett-Maitland and Jenice Lynch (February 5th 2020). Social Media, Ethics and the Privacy Paradox [Online First], IntechOpen, DOI: 10.5772/intechopen.90906.

Alberto Cano (February 19th 2020). Introductory Chapter: Data Streams and Online Learning in Social Media, Social Media and Machine Learning, Alberto Cano, IntechOpen, DOI: 10.5772/intechopen.90826.

New Zealand

The problem of disinformation in online social networks has recently received a considerable amount of attention from the research community. It has been shown that online social networks are extensively getting exploited to alter public opinion and individuals’ stance on a wide-range of topics. This study proposes an agent-based model that simulates a disinformation campaign by a group of organized users called conspirators, targeting a susceptible population, which are then opposed by a parallel organized group of users referred to as inouclators that try to act as a barrier to the spread of disinformation. The results of this study indicate that the process of inoculating a susceptible population against disinformation is mostly at the price of further polarizing the population.

AAMAS 2020

Modeling Disinformation and the Effort to Counter It: A Cautionary Tale of When the Treatment Can Be Worse Than the Disease

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

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.

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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). 

target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.

Deep Residual Reinforcement Learning

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**Abstract:**

Crowdsourcing harnesses human effort to solve computer-hard problems. Such tasks often have different levels of difficulty and workers have varying levels of skill at completing them. With a limited budget, it is important to wisely allocate the spend among the tasks and workers such that the overall outcome is as good as possible. Most existing work addresses this budget allocation problem by assuming that workers have a single level of ability for all tasks. However, this neglects the fact that tasks can belong to a variety of diverse categories and workers may have varying abilities across them. To incorporating such category-awareness, we model the interaction between the crowdsource campaign initiator and the workers as a procurement auction and propose a computationally efficient mechanism, INCARE, to achieve high-quality outcomes given a limited budget. In this presentation, we will present INCARE in detail and show the performance of INCARE on a standard real-world data set.

A Budget-Limited Mechanism for Category-Aware Crowdsourcing Systems

Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compositionality. We note that most of those works have focused on communicative bandwidth as being of primary importance. While important, it is not the only contributing factor. In this paper, we investigate the learning biases that affect the efficacy and compositionality of emergent languages. Our foremost contribution is to explore how capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.

Capacity, Bandwidth, and Compositionality in Emergent Language Learning

The presentation video of the paper "META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation" for AAMAS 2020.

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation

Candidate Selections with Proportional Fairness Constraints

We consider an online resource allocation problem where tasks with specific values, sizes and resource requirements arrive dynamically over time, and have to be either serviced or rejected immediately. Reinforcement learning is a promising approach for this, but existing work on reinforcement learning has neglected that task owners may misreport their task requirements or values strategically when this is to their benefit. To address this, we apply mechanism design and propose a novel mechanism based on reinforcement learning that aims to maximise social welfare, is strategyproof and individually rational (i.e., truthful reporting and participation are incentivised). In experiments, we show that our algorithm achieves results that are typically within 90% of the optimal social welfare, while outperforming approaches that use fixed pricing (by up to 86% in specific cases).

Strategyproof Reinforcement Learning for Online Resource Allocation

In this work, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation while they are moving. We develop a two-phase search algorithm, called SWARM-MAPF, whose first phase is inspired by swarm-based algorithms (in open regions) and whose second phase is inspired by multi-agent path-finding (MAPF) algorithms (in congested regions). In the first phase, SWARM-MAPF selects a leader among the agents and finds a path for it that is sufficiently far away from the obstacles so that the other agents can preserve the desired formation around it. It also identifies the critical segments of the leader's path where the other agents cannot preserve the desired formation and the refinement of which has thus to be delegated to the second phase. In the second phase, SWARM-MAPF refines these segments. Theoretically, we prove that SWARM-MAPF is complete. Empirically, we show that SWARM-MAPF scales well and is able to find close-to-optimal solutions.

Moving Agents in Formation in Congested Environments

Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems. However, the potential of this metaheuristic has not been utilized in Distributed Constraint Optimization Problems (DCOPs), a well-known class of combinatorial optimization problems prevalent in Multi-Agent Systems. In this paper, we present a novel population-based algorithm, Anytime Evolutionary DCOP (AED), that uses evolutionary optimization to solve DCOPs. In AED, the agents cooperatively construct an initial set of random solutions and gradually improve them through a new mechanism that considers an optimistic approximation of local benefits. Moreover, we present a new anytime update mechanism for AED that identifies the best among a distributed set of candidate solutions and notifies all the agents when a new best is found. In our theoretical analysis, we prove that AED is anytime. Finally, we present empirical results indicating AED outperforms the state-of-the-art DCOP algorithms in terms of solution quality.

Modeling Disinformation and the Effort to Counter It: A Cautionary Tale of When the Treatment Can Be Worse Than the Disease

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Deep Residual Reinforcement Learning

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Modeling Disinformation and the Effort to Counter It: A Cautionary Tale of When the Treatment Can Be Worse Than the Disease

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Next from AAMAS 2020

Deep Residual Reinforcement Learning

Similar lecture

Real-time prediction of online shoppers’ purchasing intention using random forest

Downloads