New Zealand

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.

Ilaria Giannoccaro and Pierpaolo Pontrandolfo (February 1st 2008). How Negotiation Influences the Effective Adoption of the Revenue Sharing Contract: A Multi-Agent Systems Approach, Supply Chain, Vedran Kordic, IntechOpen, DOI: 10.5772/5337.

Tomasz Babczy&#324;ski and Jan Magott (February 1st 2008). Estimation of Mean Response Time of Multi&#151;Agent Systems Using Petri Nets, Petri Net, Theory and Applications, Vedran Kordic, IntechOpen, DOI: 10.5772/5324. 

AAMAS 2020

Agent-Based Explanations in AI: Towards an Abstract Framework

understandability

multi-agent systems

explainable artificial intelligence

explainability

interpretability

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

On-line Estimators for Ad-hoc Task Allocation

"Cooperative cargo transport (e.g., two agents moving a table) is trivial for humans, but poses exceptional challenges to robots. One challenge is learning the dynamics properties of unknown cargo, which is critical for safe operations. We present an algorithm to estimate the inertial parameters of an object grasped by one or more robots in real-time. 
We model each robot's N sub-body system--- considering external and joint actuation--- using the Recursive Newton-Euler equations. A constrained Unscented Kalman Filter estimates the grasped object's mass, center of mass and moments of inertia. Our approach is validated through simulation using Astrobee, a free-flying robot."

Cooperative Real-Time Inertial Parameter Estimation

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.

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

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.

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On-line Estimators for Ad-hoc Task Allocation

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On-line Estimators for Ad-hoc Task Allocation

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How about Explainability in Human-Agent Systems

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