New Zealand


We demonstrate the Rapid Integration &amp; Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.

AAMAS 2022

Demonstrating the Rapid Integration &amp; Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities

embodied conversational agents

multiagents

virtual humans

toolkits

integration

artificial intelligence

machine learning

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021


We demonstrate the Rapid Integration & Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.

Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities

demo

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 (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).


Given a set of agents with approval preferences over each other, we study the task of finding k matchings fairly representing everyone's preferences. We model the problem as an approval-based multiwinner election where the set of candidates consists of matchings of the agents, and agents' preferences over each other are lifted to preferences over matchings. Due to the exponential number of candidates in such elections, standard algorithms for classical sequential voting rules (such as those proposed by Thiele and Phragmén) are rendered inefficient. We show that the computational tractability of these rules can be regained by exploiting the structure of the approval preferences. Moreover, we establish algorithmic results and axiomatic guarantees that go beyond those obtainable in the general multiwinner setting: Assuming that approvals are symmetric, we show that Proportional Approval Voting (PAV), a well-established but computationally intractable voting rule, becomes polynomial-time computable, and that its sequential variant, which does not provide any proportionality guarantees in general, fulfills a rather strong guarantee known as extended justified representation. Some of our algorithmic results extend to other types of compactly representable elections with an exponential candidate space.

Proportional Representation in Matching Markets: Selecting Multiple Matchings under Dichotomous Preferences


Many models have been proposed for computing a one-to-one matching between two equal-sized sets/sides of agents, each assigned with one preference list of the agents in the opposite side. The most prominent one might be the Stable Matching model. Recently, the Stable Matching model has been extended to the multi-modal setting \cite{UncertainLinearPfAziz2020,ChenEC2018Multi,Miyazaki19Jointly}, where each agent has more than one preference lists, each represents a criteria based on which the agents of the opposite side are evaluated. We use a layer to denote the set of preference lists of agents, which are based on the same criteria. Thus, the single modal matching problem has only one layer. This setting finds applications in many real-world scenarios. However, it turns out that computing a stable matching with multi-modal preferences is NP-hard and W-hard with respect to several natural parameters. Here, we introduce three position-based matching models, which minimize the ``dissatisfaction score''. We define four dissatisfaction scores, namely, the one of a single agent (the position of an agent's matching partner in his/her preference), the one of a matching pair (the sum of the dissatisfaction scores of two agents forming a matching pair), the dissatisfaction score of one side (the sum of the dissatisfaction scores of all agents in one side), and the total dissatisfaction score (the sum of all dissatisfaction scores of all agents). The first model minimizes the total respective dissatisfaction score over all layers, while the second minimizes the maximum of the respective score over all layers. The third model seeks for a matching $M$ which is \PO, meaning that there does not exist a matching $M^\prime$, which is at least as good as $M$ with respect to the respective dissatisfaction score in all layers, but is strictly better in at least one layer.
We present diverse complexity results for these three models, among others, polynomial-time solvability for the first model. We also investigate the generalization which given an upper bound on the dissatisfaction score, computes a matching involving subsets of agents and a subset of layers. Hereby, we mainly focus on the parameterized complexity with respect to parameters such as the size of agent subsets, or the size of the layer subset and achieve fixed-parameter tractability as well as intractability results.

Position-based Matching with Multi-Modal Preferences


In the simplest game-theoretic formulation of Schelling's model of segregation on graphs, agents of two different types each select their own vertex in a given graph so as to maximize the fraction of agents of their type in their occupied neighborhood. Two ways of modeling agent movement here are either to allow two agents to swap their vertices or to allow an agent to jump to a free vertex.
The contributions of this paper are twofold. First, we prove that deciding the existence of a swap-equilibrium and a jump-equilibrium in this simplest model of Schelling games is NP-hard, thereby answering questions left open by Agarwal et al. [AAAI '20] and Elkind et al. [IJCAI '19]. Second, we introduce two measures for the robustness of equilibria in Schelling games in terms of the minimum number of edges or the minimum number of vertices that need to be deleted to make an equilibrium unstable. We prove tight lower and upper bounds on the edge- and vertex-robustness of swap-equilibria in Schelling games on different graph classes.

Equilibria in Schelling Games: Computational Hardness and Robustness


Smart Cities introduce novel problems related to the management of inordinate amounts of data of different types and sources. Hence, solutions are required where data may be committed by any actor, being, at the same time, freely available to the community. With this in mind, this study introduces KnowLedger, a multi-agent system (MAS) blockchain to store structured data, where a new protocol, entitled as Proof-of-Confidence, is used to award those who commit accurate and reliable data. The obtained results show that the MAS proved to be beneficial not only for data collection but mainly for the maintenance of the blockchain, with the agents being responsible for the entire workload, from data collection to mining.

KnowLedger - A Multi-Agent System Blockchain for Smart Cities Data


Machine Learning (ML) focuses on giving machines the ability to forecast, predict, or classify without being explicitly programmed to do so. To achieve such goals, large amounts of data are used to conceive models that can adapt to unseen data and to new scenarios. However, applying ML models to real-world business domains is a resource-intensive and time-consuming effort. Automated machine learning (AutoML) emerged as a way to ease such processes. With this in mind, this study introduces a multi-agent system (MAS) that autonomously go through the entire ML pipeline, with different entities being responsible for the data collection process, for preprocessing the data, and for deploying the best candidate ML model. The conceived MAS is currently implemented in a real-world setting, addressing important societal challenges raised by big urban centers. The obtained results show that this solution proved to be beneficial not only for the data collection and pre-processing tasks, but also for the automated execution of ML models.

A Multi-Agent System for Automated Machine Learning


Greenhouse gas (GHG) emission reduction is an important and necessary goal; currently, different policies to reduce GHG emissions in maritime transport are being discussed. Amongst policies, like carbon taxes or carbon intensity targets, it is hard to determine which policies can successfully reduce GHG emissions while allowing the industry to be profitable. We introduce an agent-based maritime transport simulator to investigate the effectiveness of two decarbonisation policies by simulating a maritime transport operator's trade pattern and fleet make-up changes as a reaction to taxation and fixed targets. This first of its kind simulator allows to compare and quantify the difference of carbon reduction policies and how they affect shipping operations.

An Agent-Based Simulator for Maritime Transport Decarbonisation


Modern intelligent systems researchers form hypotheses about system behavior
and then run experiments using one or more independent variables to test their
hypotheses. We present SIERRA, a novel framework structured around that idea
for accelerating research developments and improving reproducibility of
results. SIERRA makes it easy to quickly specify the independent variable(s)
for an experiment, generate experimental inputs, automatically run the
experiment, and process the results to generate deliverables such as graphs
and videos. SIERRA provides reproducible automation independent of the
execution environment (HPC hardware, real robots, etc.) and targeted platform
(arbitrary simulator or real robots), enabling exact experiment replication
(up to the limit of the execution environment and platform). It employs a
deeply modular approach that allows easy customization and extension of
automation for the needs of individual researchers, thereby eliminating manual
experiment configuration and result processing via throw-away scripts.

SIERRA: A Modular Framework for Research Automation

Ad-hoc reasoning models are recurrently used to solve some of our daily tasks. Intending to avoid worthless investments or spending valuable resources, these smart systems require a proper evaluation before acting in the real world. In this paper, we demonstrate AdLeap-MAS, a novel framework focused on enabling quick and easy testing of smart algorithms in ad-hoc reasoning domains.

AdLeap-MAS: An Open-source Multi-Agent Simulator for Ad-hoc Reasoning

**Read paper on the following link:**

https://ifaamas.org/Proceedings/aamas2022/pdfs/p1914.pdf

**Abstract:**



LBfT: Learning Bayesian Network Structures from Text in Autonomous Typhoon Response Systems


Objective Clinical Structured Examination (OSCE) is used to assess multiple competencies in medical pedagogy such as efficiently eliciting relevant clinical history from a patient. Clinical interviewing is done in a question and answer fashion, making it amenable to computer simulation. We introduce Chameleon, a framework to create virtual patients in an OSCE setting using the conversational agent platform Dialogflow CX. Our framework consists of a generic chatbot that is capable of answering most questions (in a classic non-specific clinical interview) and which can be expanded to capture any clinical presentation, e.g. a patient with backpain.

Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities

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Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities

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Next from AAMAS 2022

Proportional Representation in Matching Markets: Selecting Multiple Matchings under Dichotomous Preferences

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Data Management for Artificial Intelligence and Machine Learning Implementation Across the Department of the Navy

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PRESENTATIONS

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COMPANY

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Downloads