New Zealand

Alberto Giretti, Alessandro Carbonari and Berardo Naticchia (April 20th 2012). A Spatio-Temporal Bayesian Network for Adaptive Risk Management in Territorial Emergency Response Operations, Bayesian Networks, Wichian Premchaiswadi, IntechOpen, DOI: 10.5772/38620. 

Artur Chrobak, Grzegorz Ziółkowski and Dariusz Chrobak (October 17th 2019). Application of Local Information Entropy in Cluster Monte Carlo Algorithms, Theory, Application, and Implementation of Monte Carlo Method in Science and Technology, Pooneh Saidi Bidokhti, IntechOpen, DOI: 10.5772/intechopen.88627. 

Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption --- algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder \textit{after} incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.

AAMAS 2020

On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities

smart and connected communities

decentralized algorithms

emergency response management

monte carlo tree search

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.

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). 

Timely evacuation is a standard recommendation by local agencies before disaster events such as hurricanes, which have enough advance notice. However, it has been observed in many recent disasters (e.g., Sandy), that only a small fraction of the population evacuates in time. Recent work by social scientists has examined the factors that influence household evacuation decisions; in addition to individual factors it has been found that peer effect plays a role in this decision but in two opposing ways. Specifically, households are motivated to evacuate if their neighbors evacuate. However, if too many neighbors leave then some households have concerns of looting and crime, and they choose not to evacuate. This makes the dynamics of evacuation very complex. In this paper, we use a detailed agent based model to study the dynamics of evacuation in Virginia's coastal region. We use data from a large survey and social contagion and collective action theories to develop the model. We evaluate different strategies to increase evacuation.

Increasing Evacuation During Disaster Events

The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years. Much of this effort has focused on the single-agent setting, in which an agent maximizes a predefined extrinsic reward function. However, a long-term question inevitably arises: how will such independent agents cooperate when they are continually learning and acting in a shared multi-agent environment? Observing that humans often provide incentives to influence others' behavior, we propose to equip each RL agent in a multi-agent environment with the ability to give rewards directly to other agents, via a learned incentive function. Each agent learns the incentive function by considering how the given incentives affect its own extrinsic objective, through the learning of agents who receive incentives. We demonstrate in experiments that such agents significantly outperform policy gradient agents and opponent-shaping agents in a small yet challenging general-sum Markov game. Our work points toward a more general research program of endowing agents with expanded capabilities for incentivizing others to ensure the common good in a multi-agent future.

Learning to Incentivize Other Learning Agents

This paper proposes an approach that aims to automatically visualize large-scale text-based online discussions. The ultimate goal is to develop an automatic visualization tool which helps to understand the content of large-scale text-based online discussions. To support this au- tomatic visualization tool, an effective discussion visualization approach is needed. Toward this end, we propose a discussion visualization method using D3.js which is a JavaScript libraly. The proposed method employs issue-based information system (IBIS), as a suitable format for struc- turing online discussions. By visualizing based on the IBIS discussion structure, it becomes possible to enhance the intuitive understanding of the contents of discussions and to find the active threads intuitively. In particular, this paper aims to support the understanding of specific topics that users are interested in. In order to evaluate the proposed approach, a set of experiments has been conducted on the data collected from the discussions in the online discussion support system called D-Agree. The experimental results demonstrate that the proposed approach is effective for supporting the understanding of discussion contents.

An Automated Tool for Visualizing Large-Scale Online Discussions

Exploiting Simulation For Mass Programming and Engineering - The JaCaMo-SIM Platform

Learning Correlation Functions on Mixed Data Sequences for Computer Architecture Applications

Personal Agents (PAs) have longly been explored as assistants to support users in their daily activities. Surprisingly, few works refer to the adoption of PAs in the healthcare domain, where they can assist physicians’ activities reducing medical errors. Although literature proposes different approaches for modelling and engineering PAs, none of them discusses how they can be integrated with cognitive services in order to empower their reasoning capabilities. In this paper we present an integration model, specifically devised for healthcare applications, that enhances Belief-Desire-Intention agents rea- soning with advanced cognitive capabilities. As a case study, we adopt this integrated model in the critical care path of trauma resuscitation, stepping forward to the vision of Smart Hospitals.

Complementing Agents with Cognitive Services: a case study in Healthcare

The collection of large-scale people opinions through discussion support system is an emerging approach for SDGs cooperation that aims to solve wicked problems in the 21st century by facilitating the discussion and making collective decision. Previous research centered on gathering large-scale people opinions considering a stable society like Japan. Here, we focus on usage and application of crowd discussion support system into fragile and conflict-affected city like Kabul, Afghanistan. Our paper is especially focused on the international experiment transfer of “D-Agree” usage and application for town meeting and city issues in five selected district of Kabul city, being presented with an opportunity of experimental transfer by Industry-Academia-Government collaboration. Our action research is based on the soft system methodology utilizing online crowd discussion system in which people can discuss their municipal district issues online. The content includes the background, process and formalization of the collaboration and reports on the outline of the societal experiment of D-Agree with public citizens officials at KM and private citizen in selected 5 districts of Kabul city. The final objective of our study is to evaluate facilitation of citizens discussion to district issues and evaluate AI-based facilitation of our system.

Usage & application of AI-based facilitation discussion support system for city issues and town meeting: Kabul experimental view

One of the fundamental challenges of governance is deciding when and how to intervene in multi-agent systems in order to impact group-wide metrics of success. This is particularly challenging when proposed interventions are novel and expensive. For example, one may wish to modify a building’s layout to improve the efficiency of its escape route. Evaluating such interventions would generally require access to an elaborate simulator, which must be constructed ad-hoc for each environment, and can be prohibitively costly or inaccurate. Here we examine a simple alternative: Optimize By Observational Extrapolation (OBOE). The idea is to use observed behavioural trajectories, without any interventions, to learn predictive models mapping environment states to individual agent outcomes, and then use these to evaluate and select changes. We evaluate OBOE in socially complex gridworld environments and consider novel physical interventions that our models were not trained on. We show that neural network models trained to predict agent returns on baseline environments are effective at selecting among the interventions. Thus, OBOE can provide guidance for challenging questions like: “which wall should I tear down in order to minimize the Gini index of this group?”

Should I tear down this wall? Optimizing social metrics by evaluating novel actions

Automatic Classification of Freely-Offered Opinions

Explain how to extract discussion structure which is not abstract

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Increasing Evacuation During Disaster Events

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Increasing Evacuation During Disaster Events

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Semi-Supervised Learning with Support Isolation by Small-Paced Self-Training

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