Bernhard Schneider (February 1st 2010). Efforts in Agent-based Simulation of Human Panic Behaviour: Reference Model, Potential, Prospects, Modelling, Simulation and Optimization, Gregorio Romero Rey and Luisa Martinez Muneta, IntechOpen, DOI: 10.5772/7653. 

Enrique Kremers, Jose Maria Gonzalez De Durana, Oscar Barambones, Pablo Viejo and Norbert Lewald (July 28th 2011). Agent-Based Simulation of Wind Farm Generation at Multiple Time Scales, Wind Farm - Impact in Power System and Alternatives to Improve the Integration, Gastón Orlando Suvire, IntechOpen, DOI: 10.5772/16531.

Will Browne, Dan Scott and Charalambos Ioannides (January 1st 2008). Abstraction for Genetics-Based Reinforcement Learning, Reinforcement Learning, Cornelius Weber, Mark Elshaw and Norbert Michael Mayer, IntechOpen, DOI: 10.5772/5281.

New Zealand

&quot;Agent-based simulations of social media platforms often need to be run for many repetitions at large scale. Often, researchers must compromise between available computational resources (memory, run-time), the scale of the simulation, and the quality of its predictions.

As a step to support this process, we present a systematic exploration of simplifications of agent simulations across a number of dimensions suitable for social media studies. Simplifications explored include sub-sampling, implementing agents representing teams or groups of users, simplifying agent behavior, and simplifying the environment.

We also propose a tool that helps apply simplifications to a simulation model, and helps find simplifications that approximate the behavior of the full-scale simulation within computational resource limits.

We present experiments in two social media domains, GitHub and Twitter, using data both to design agents and to test simulation predictions against ground truth. Sub-sampling agents often provides a simple and effective strategy in these domains, particularly in combination with simplifying agent behavior, yielding up to an order of magnitude improvement in run-time with little or no loss in predictive power. Moreover, some simplifications improve performance over the full-scale simulation by removing noise.

We describe domain characteristics that may indicate the most effective simplification strategies and discuss heuristics for automatic exploration of simplifications.&quot;


AAMAS 2020

Optimization of Large-scale Agent-based Simulations through Automated Abstraction and Simplification

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

"Agent-based simulations of social media platforms often need to be run for many repetitions at large scale. Often, researchers must compromise between available computational resources (memory, run-time), the scale of the simulation, and the quality of its predictions.

As a step to support this process, we present a systematic exploration of simplifications of agent simulations across a number of dimensions suitable for social media studies. Simplifications explored include sub-sampling, implementing agents representing teams or groups of users, simplifying agent behavior, and simplifying the environment.

We also propose a tool that helps apply simplifications to a simulation model, and helps find simplifications that approximate the behavior of the full-scale simulation within computational resource limits.

We present experiments in two social media domains, GitHub and Twitter, using data both to design agents and to test simulation predictions against ground truth. Sub-sampling agents often provides a simple and effective strategy in these domains, particularly in combination with simplifying agent behavior, yielding up to an order of magnitude improvement in run-time with little or no loss in predictive power. Moreover, some simplifications improve performance over the full-scale simulation by removing noise.

We describe domain characteristics that may indicate the most effective simplification strategies and discuss heuristics for automatic exploration of simplifications."


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). 

The Smart City and Internet-of-Things revolutions enable the collection of various types of data in real-time through sensors. This data can be used to improve the decision tools and simulations used by city planners. This work presents a new framework for real-time traﬃc simulation integrating an agent-based methodology with live CCTV and other sensor data while respecting the privacy regulations. The framework simulates traﬃc ﬂows of pedestrians, vehicles and bicycles and their interactions. The approach has been applied in Liverpool (NSW, Australia) showing promising preliminary results and can easily ingest additional sensor data, e.g. air quality.


Towards Agent-Based Traffic Simulation Using Live Data from Sensors for Smart Cities

This presentation introduces a teaching model BC-MDP to cultivate a population by tweaking the environment dynamics. The proposed BC-MDP combines the Behaviour Cultivation core with the recent advances of Curriculum MDPs. This allows BC-MDP to address several shortcomings of the classical BC, while preserving its strengths, such as the freedom from the teacher-learner value alignment. Our model exploits the knowledge of the learner population adaptation process to induce and proliferate a desired behaviour throughout the population. We experimentally show its effectiveness, and retention of key positive features of both BC and Curriculum-MDP.


Teaching Multiple Learning Agents by Environment-Dynamics Tweaks

"Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the
rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community ""toxic"". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities."


Norm violation identification in online communities : A study of Stack Overflow comments

We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions. An example multiobjective Markov Decision Process (MOMDP) is used to demonstrate that under such conditions these approaches may be unable to discover the policy which maximises the Scalarised Expected Return, and in fact may converge to a Pareto-dominated solution. We discuss several alternative methods which may be
more suitable for maximising SER in MOMDPs with stochastic transitions.

A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions

Autonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior is aligned with our values and norms. Hence, an AA will need to estimate the values and norms of the humans it interacts with, which is not a straightforward task when solely observing an agent's behavior. This paper analyses to what extent an AA is able to estimate the values and norms of a simulated human agent (SHA) based on its actions in the ultimatum game. We present two methods to reduce ambiguity in profiling the SHAs: one based on search space exploration and another based on counterfactual analysis. We found that both methods are able to increase the confidence in estimating human values and norms, but differ in their applicability, the latter being more efficient when the number of interactions with the agent is to be minimized. These insights are useful to improve the alignment of AAs with human values and norms.

Improving Confidence in the Estimation of Values and Norms

A digital twin is a digital representation of a physical asset reproducing its data model, its behaviour and its communication with other physical assets. Although most of their concrete applications can be found mainly in the industrial context, healthcare represents another relevant area where digital twins can have a disruptive impact. The main research question tackled by this paper is about the integration of digital twins with agents and Multi-Agent Systems (MAS) technologies in healthcare, discussing what the benefits are and how such an integration could be realised.


On the Integration of Agents and Digital Twins in Healthcare

In many multi-agent interactions in the real world, agents receive payoffs over multiple distinct criteria; i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. In this work, we present the first study of the effects of such opponent modelling on multi-objective multi-agent interactions with non-linear utilities. Specifically, we consider multi-objective normal form games with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute a novel actor-critic formulation to allow reinforcement learning of mixed strategies in this setting, along with an extension that incorporates opponent policy reconstruction using conditional action frequencies. Empirical results in five different MONFGs demonstrate that opponent modelling can drastically alter the learning dynamics in this setting. When equilibria are present opponent modelling can confer significant benefits on agents that implement it. However, when there are no Nash equilibria, opponent modelling can have adverse effects on utility, and has a neutral effect at best (after extensive hyper-parameter optimisation).


Opponent Modelling using Policy Reconstruction for Multi-Objective Normal Form Games

We compare synthetic population-based travel demand modeling with the state of the art
travel demand models used by metropolitan planning offices in the United States. Our 
comparison of the models for three US cities shows that synthetic population-based
models match the state of the art models closely for the temporal trip distributions and
the spatial distribution of destinations. The advantages of the synthetic population-based 
method are that it provides greater spatial resolution, can be generalized to any region,
and can be used for studying correlations with demographics and activity types, which are
useful for modeling the effects of policy changes.

Improved Travel Demand Modeling with Synthetic Populations

We study the problem of two competing camps aiming to maximize the adoption of their respective opinions, by optimally investing in nodes of a social network in two phases. The final opinion of a node in phase 1 acts as its bias in phase 2, and this bias determines the effectiveness of a camp's investment on the node. Using an extension of Friedkin-Johnsen model of opinion dynamics, we formulate the camps' utility functions. We show the existence and polynomial time computability of Nash equilibrium under reasonable assumptions. Using simulations, we quantify the effects of the nodes' biases and the weightage attributed to them, as well as that of a camp deviating from its equilibrium strategy.


A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks

We define noisy combinatorial markets, a specific class of combinatorial markets and introduce a model to study them. We present a learning methodology we developed to learn competitive equilibria of noisy combinatorial markets. We summarize preliminary experimental results to evaluate the performance of our learning algorithms.


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