New Zealand


Interactive dynamic influence diagrams (I-DIDs) are a general framework
for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system
that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the
I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms.

AAMAS 2022

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms

interactive dynamic influence diagrams; evolutionary algorithms; interactive decision systems

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/ceby/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>

Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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).


Swarming behaviours are ubiquitous in nature, and an important topic to teach and learn. In the learning sciences domain of complex systems understanding, studies have shown that novices tend to assume that centralized control and leadership should exist whenever they see complex patterns. In this paper, we present an educational framework consisting of a swarm of virtual and/or physical agents (robots) as a game-based learning environment to introduce the underlying rules of swarm behaviours. 


Cellulan World: Interactive platform to learn swarm behaviors


This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated task and motion planning and explainable AI. JEDAI helps users create high-level, intuitive plans while ensuring that they will be executable by the robot. It also provides users customized explanations about errors and helps improve their understanding of AI planning as well as the limits and capabilities of the underlying robot system.

JEDAI: A System for Skill-Aligned Explainable Robot Planning


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


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.

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