New Zealand

Ümit Ulusoy, Mehmet Serdar Güzel and Erkan Bostanci (April 22nd 2020). A Q-Learning-Based Approach for Simple and Multi-Agent Systems, Multi Agent Systems - Strategies and Applications, Ricardo López - Ruiz, IntechOpen, DOI: 10.5772/intechopen.88484.

Mengchun Xie (January 14th 2011). Cooperative Behavior Rule Acquisition for Multi-Agent Systems by Machine Learning, Advances in Reinforcement Learning, Abdelhamid Mellouk, IntechOpen, DOI: 10.5772/13280. 

Ling Cen, Minghui Dong, Haizhou Li Zhu Liang Yu and Paul Chan (February 1st 2010). Machine Learning Methods in the Application of Speech Emotion Recognition, Application of Machine Learning, Yagang Zhang, IntechOpen, DOI: 10.5772/8613.

Malin Bjornsdotter (February 1st 2010). Machine Learning for Functional Brain Mapping, Application of Machine Learning, Yagang Zhang, IntechOpen, DOI: 10.5772/8605.

AAMAS 2020

Voting for ensemble classifiers

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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). 

Implementation of the Multi-Agent Microservices concept using the CArtAgO framework to represent virtual resources exposed by the agent, with a Linked Data represenation of each resource.


Delivering Multi-Agent MicroServices using CArtAgO

This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm’s ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.


Evolving Meta-Level Reasoning with Reinforcement Learning  and A* for Coordinated Multi-Agent Path-planning

Fear of Punishment Promotes the Emergence of Cooperation and Enhanced Social Welfare in Social Dilemmas

Hemaspaandra [22] celebrated the quite close relationship between computational social choice and computational complexity as a two-way street from which both areas benefited in the past, and expressed his hope that the areas become best friends forever. Later on, Rothe [38] celebrated the prominent Borda voting rule and surveyed recent advances on the complexity of problems related to the three most fundamental models of tampering with elections—namely, via manipulation, control, and bribery—and even related to using Borda beyond voting: in fair division and coalition formation in hedonic games. But now the party is over: no more celebration! Instead, we present a common criticism regarding computational social choice persistently making use of worst-case complexity. To overcome this shortcoming, we propose our blue sky idea of applying to problems from computational social choice the method of smoothed analysis due to Spielman and Teng [43, 44] and also
used by Bläser and Manthey [7], as some sort of a middle ground between the worst-case and the average-case analysis of algorithms.

Towards Reality: Smoothed Analysis in Computational Social Choice

Forecasting the future is a notoriously difficult task. One way to address this challenge is to "hybridize" the forecasting process, combining forecasts from a crowd of humans, as well as one or more machine models. However, an open challenge remains in how to optimally aggregate inputs from these pools into a single forecast. We proposed anchor attention for this type of sequence summary problem. Each forecast is represented by a trainable embedding vector. An anchor attention score is used to determine input weights. We show that our method learned a good representation for both forecaster and question, and outperforms the current state-of-the-art aggregation approaches.

Anchor Attention for Hybrid Crowd Forecasts Aggregation

This paper presents a multi-robot long-term planning ap- proach under uncertainty on the duration of tasks. The proposed method- ology takes advantage of a novel modeling formalism for robot teams: generalized stochastic Petri nets with rewards (GSPNR). A GSPNR al- lows for unified modeling of action selection and uncertainty on the du- ration of action execution. At the same time, it allows for goal specifi- cation through the use of transition rewards and rewards per time unit. Our proposed approach exploits the well-defined semantics provided by Markov reward automata in order to synthesize policies for GSPNRs. It builds on a state-of-the-art method to compute the long-run average reward, extending it in order to allow for the extraction of the optimal policy. We provide an empirical evaluation of the proposed approach on a simulated multi-robot monitoring problem, evaluating its performance and scalability. The results show that, the obtained policy outperforms a carefully hand-crafted policy.


Long-Run Multi-Robot Planning with Uncertain Task Durations

Graph Neural Networks for Decentralized Path Planning

We consider the influence maximization problem (selecting k seeds in a network maximizing the expected total influence) on undirected graphs under the linear threshold model. On the one hand, we prove that the greedy algorithm always achieves a (1−(1−1/k)^k+Ω(1/k^3))-approximation, showing that the greedy algorithm does slightly better on undirected graphs than the generic (1−(1−1/k)^k) bound which also applies to directed graphs. On the other hand, we show that substantial improvement on this bound is impossible by presenting an example where the greedy algorithm can obtain at most a (1−(1−1/k)^k+O(1/k^0.2)) approximation. This result stands in contrast to the previous work on the independent cascade model. Like the linear threshold model, the greedy algorithm obtains a (1−(1−1/k)^k)-approximation on directed graphs in the independent cascade model. However, Khanna and Lucier showed that, in undirected graphs, the greedy algorithm performs substantially better: a (1−(1−1/k)^k+c) approximation for constant c>0. Our results show that, surprisingly, no such improvement occurs in the linear threshold model. Finally, we show that, under the linear threshold model, the approximation ratio (1−(1−1/k)^k) is tight if 1) the graph is directed or 2) the vertices are weighted. In other words, under either of these two settings, the greedy algorithm cannot achieve a (1−(1−1/k)^k+f(k))-approximation for any positive function f(k). The result in setting 2) is again in a sharp contrast to Khanna and Lucier's (1−(1−1/k)^k+c)-approximation result for the independent cascade model, where the (1−(1−1/k)^k+c) approximation guarantee can be extended to the setting where vertices are weighted. We also discuss extensions to more generalized settings including those with edge-weighted graphs.


Limitations of Greed: Influence Maximization in Undirected Networks Re-visited

MB-DPOP is an important complete algorithm for solving Distributed Constraint Optimization Problems (DCOPs) by exploiting a cycle-cut idea to implement memory-bounded inference. However, each cluster root in the algorithm is responsible for enumerating all the instantiations of its cycle-cut nodes, which would cause redundant inferences when its branches do not have the same cycle-cut nodes. Additionally, a large number of cycle-cut nodes and the iterative nature of MB-DPOP further exacerbate the pathology. As a result, MB-DPOP could suffer from huge coordination overheads and cannot scale up well. Therefore, we present RMB-DPOP which incorporates several novel mechanisms to reduce redundant inferences and improve the scalability of MB-DPOP. First, using the independence among the cycle-cut nodes in different branches, we distribute the enumeration of instantiations into different branches whereby the number of nonconcurrent instantiations reduces significantly and each branch can perform memory bounded inference asynchronously. Then, taking the topology into the consideration, we propose an iterative allocation mechanism to choose the cycle-cut nodes that cover a maximum of active nodes in a cluster and break ties according to their relative positions in a pseudo-tree. Finally, a caching mechanism is proposed to further reduce unnecessary inferences when the historical results are compatible with the current instantiations. We theoretically show that with the same number of cycle-cut nodes RMB-DPOP requires as many messages as MB-DPOP in the worst case and the experimental results show our superiorities over the state-of-the-art.


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Delivering Multi-Agent MicroServices using CArtAgO

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Delivering Multi-Agent MicroServices using CArtAgO

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Voting with Random Classifiers in Ensembles (VORACE)

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