New Zealand

Junmei Zhong and Xiu Yi (February 28th 2020). Categorizing Patient Disease into ICD-10 with Deep Learning for Semantic Text Classification [Online First], IntechOpen, DOI: 10.5772/intechopen.91292. 

Antonia Kyriakopoulou (August 1st 2008). Text Classification Aided by Clustering: a Literature Review, Tools in Artificial Intelligence, Paula Fritzsche, IntechOpen, DOI: 10.5772/6083. 

Etsuji Tomita, Tatsuya Akutsu and Tsutomu Matsunaga (January 8th 2011). Efficient Algorithms for Finding Maximum and Maximal Cliques: Effective Tools for Bioinformatics, Biomedical Engineering, Trends in Electronics, Communications and Software, Anthony N. Laskovski, IntechOpen, DOI: 10.5772/13245. 

AAMAS 2020

Automatic Classification of Freely-Offered Opinions

freely-offered opinion

questionnaire

maximal clique

clustering

text classification

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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). 

Explain how to extract discussion structure which is not abstract

Extracting Discussion Structures for Automated Facilitation Agents

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems. Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to understanding the norms in human society. This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events extracted from news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.

Mining International Political Norms from the GDELT Database

This paper proposes an approach that aims to extract online discussions structure and to reduce the extraction model size simultaneously. The ultimate goal is the development of an automated facilitation agent that automatically facilitates online discussions. In large-scale online discussions, it becomes necessary to have a facilitator. However, facilitates large-scale online discussions with hundreds or thousands of participants is an intricate problem, and in dynamic discussions, it be- comes impossible for human facilitators to keep an eye on the flow of discussions. As a result, we propose the development of an automated facilitator agent that automatically facilitates online discussions. In order for the automated facilitation agent to function properly, it needs to dynamically extract the structure of online discussions. Towards this end, we use Bidirectional Encoder Representative from Transformers(BERT) and deep neural networks for extracting the structure of online discus- sions. In order to evaluate the proposed approach, we use a dataset based on extracting different posts from a large-scale online discussion system called D-Agree. The experimental results show that the proposed approach is efficient for extracting the structure of online discussions where the link extraction model size is much smaller than the existing methods.

Deep Bidirectional Transformers for Online Discussion Understanding

The enactment of dynamic workflows may take advantage of the multi-agent system paradigm. The approach presented in this pa- per allows exploiting a high-level BPMN definition to generate several agent organisations that can enact the BPMN workflow. The availabil- ity of different solutions (i.e. organisations) enables the optimisation and adaptation features of the approach. The mapping of the initial workflow to organisations starts with the automatic generation of goals from the BPMN, and it exploits a metamodeling approach to generate MOISE organisation definitions.

Goal-driven Adaptation of MOISE Organizations for Workflow Enactment

Many to Many Matching of Agents with Nonlinear Utility Function

Social influence is an essential aspect of human social interaction. Influence propagation modeling has been extensively studied and applied in various research fields, such as the maximization of product adoption, the spread of infectious diseases, etc. The primary sources of information in any social network are the influential nodes that propagate this information further. Our study gives a method to identify the strong influencers in a network and dynamically predict the links/interactions made by them during information propagation. Previously, many efforts have been made to solve the link prediction problem in social networks such as the Adamic Adar index, based on the degree of common connecting neighbors; however, they disregard the fact that social networks can be dynamic over time—any network dataset collected by a snapshot at a particular instant of time may be incomplete and not reflect all the previous links formed. This may in turn result in false predictions. Our approach considers the current dynamics of the social network where information propagated by an agent leads to the prediction of new links with other nodes. Taking opinion propagation as an essential feature, we test our algorithm on Stanford’s Facebook network dataset and compare the results with the previous Adamic Adar index. The results obtained cover all the links predicted by Adamic Adar, as well as some new links formed dynamically. This is indicative that considering dynamic link formation is more efficient and helps address the problem of the incompleteness of network datasets reflecting the state at a time instant.

Dynamic Interactions by Strong Influencers in Social Networks using Opinion Propagation

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. The proposed approach circumvents the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need for mutually consistent actions) by relying on the ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning is decentralized and requires no communication between the agents. The latter only observe their own history of action/reward pairs.

Learning to Persist or Switch: Efficient and Fair Allocations in Large-scale Multi-agent Systems

We consider the problem of forming a testbed of elections to be used
for numerical experiments (such as testing algorithms or estimating
the frequency of a given phenomenon). We seek elections that
come from well-known statistical distributions and are as diverse
as possible. To this end, we define a (pseudo)metric over elections,
generate a set of election instances, and measure distances between
them, to assess how diverse they are. Finally, we show how to use
these elections to test election-related algorithms.

Drawing a Map of Elections

"Autonomous agents promise users of a personalized future, allowing
them to direct their attention to tasks most meaningful to them.
However, the demands of personalization stand unfulfilled by current
agent training paradigms such as machine learning, which
require many orders of data to train agents on a single task. In
sequential decision making domains, Reinforcement Learning (RL)
enables this need, when a priori training of desired behaviors is
intractable. Prior work has leveraged user input to train agents
by mapping them to numerical reward signals. However, recent
approaches have identified inconsistent human feedback as a bottleneck
to achieving best-case performance. In this work, we present
empirical evidence to show that human perception affected by
contrast effects distorts their feedback to Reinforcement Learning
agents. Through a set of studies involving 900 participants from
Amazon Mechanical Turk who were asked to give feedback to RL
agents, we show that participants significantly underrate an agent’s
actions after being exposed to an agent of higher competence on the
same task. To understand the significance of this effect on agent performance
during training, we then simulate trainers that underrate
actions of an agent based on past performance – creating a systematically
skewed feedback signal – integrated into an actor-critic
framework. Our results show that agent performance is reduced by
up to 98% in the presence of systematic skews in human feedback in
Atari environments. Our work provides a conceptual understanding
of a source of inconsistency in human feedback, thus informing the
design of human-agent interactions."

Yesterday’s Reward is Today’s Punishment: Contrast Effects in Human Feedback to Reinforcement Learning Agents

Explainability has been a core research topic in AI for decades and therefore it is surprising that the current concept of Explainable AI (XAI) seems to have been launched as late as 2016. This is a problem with current XAI research because it tends to ignore existing knowledge and wisdom gathered over decades or even centuries by other relevant domains. This paper presents the notion of Contextual Importance and Utility (CIU), which is based on known notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.

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Examining Bias in Opinion Summarisation through the Perspective of Opinion Diversity

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Next from AAMAS 2020

Extracting Discussion Structures for Automated Facilitation Agents

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Examining Bias in Opinion Summarisation through the Perspective of Opinion Diversity

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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