New Zealand


Evacuation plans are designed to move people to safety in case of a disaster. It mainly consists of two components: routing and scheduling. However, the joint optimization of these two components with the goal of minimizing total evacuation time is a computationally hard problem, specifically when the problem instance is large. Moreover, often in disaster situations, there is uncertainty regarding the passability of roads throughout the evacuation time period. In this paper, we present a way to model the time-varying risk associated with roads in disaster situations. Then, we present a heuristic method that performs the joint optimization over routing and scheduling by iteratively solving subproblems of the same form. We use real world road network and population data from Harris County in Houston, Texas and apply our heuristic to find evacuation routes and schedules for the area. We show that the proposed method is able to find good solutions within a reasonable amount of time. We also perform agent-based simulations of the evacuation using these solutions to evaluate their quality and efficacy.

AAMAS 2022

Data-driven Agent-based Models for Optimal Evacuation of Large Metropolitan Areas for Improved Disaster Planning

individual and social utility

large neighborhood search

mixed integer program

evacuation

routing

heuristic

scheduling

simulation

**ICWSM-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.**

**The Workshop and Tutorial Sessions are accessible to those that registered separately for these sessions. If you registered to attend but are not able to access the these sessions from the [Schedule tab](https://underline.io/events/291/schedule?day=2022-06-06T04%3A00%3A00.000Z), please email icwsm@underline.io
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The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.


ICWSM, now in its sixteenth year, has become one of the premier venues for computational social science, and previous years of ICWSM have featured papers, posters, and demos that draw upon network science, machine learning, computational linguistics, sociology, communication, and political science. The uniqueness of the venue and the quality of submissions have contributed to a rapidly growing conference, and a competitive acceptance rate of approximately 20% for full-length research papers published in the proceedings by the Association for the Advancement of Artificial Intelligence (AAAI).


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A ticket is required to attend the event, please use the below URL to register for the event if you have not already done so:

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Registration Is Required

ICWSM 2022

The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.

poster

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).
<br>
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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/

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).


This study proposes a new scheme for implementing actual data into artificial market simulations at the level of trader agents. Because humans can introduce bias or overlook the important features of actual traders, we implemented the actual data and automated the strategy learning (imitating) of agents using machine learning (ML). We then ran artificial market simulations in the treader model, which imitates the actual trading behaviors in an ML architecture. Through this study, we demonstrate the potentials and limitations of the proposed scheme.

Implementation of Actual Data for Artificial Market Simulation


Individual rationality, which involves maximizing expected individual returns, does not always lead to high-utility individual or group outcomes in multi-agent problems. For instance, in multi-agent social dilemmas, Reinforcement Learning (RL) agents trained to maximize individual rewards converge to a low-utility mutually harmful equilibrium. In contrast, humans evolve useful strategies in such social dilemmas. Inspired by ideas from human psychology that attribute this behavior to the status-quo bias, we present a status-quo loss (𝑆𝑄𝐿𝑜𝑠𝑠) and the corresponding policy gradient algorithm that incorporates this bias in an RL agent. We demonstrate that agents trained with 𝑆𝑄𝐿𝑜𝑠𝑠 learn high-utility policies in several social dilemma matrix games (Prisoner’s Dilemma, Matching Pennies, Chicken Game). To apply SQLoss to visual input games where cooperation and defection are determined by a sequence of lower-level actions, we present GameDistill, an algorithm that reduces a visual input game to a matrix game. We empirically show how agents trained with SQLoss on GameDistill reduced versions of Coin Game and Stag Hunt learn high-utility policies. Finally, we show that 𝑆𝑄𝐿𝑜𝑠𝑠 extends to a 4-agent setting by demonstrating the emergence of cooperative behavior in the popular Braess’ paradox.

Status-quo policy gradient in Multi-Agent Reinforcement Learning

Artificial social agents (ASAs) are computer-based autonomous entities who interact with humans in a range of social roles, including advising, coaching, and consumer support in education and health. While there are many discussions around the ethical use of Artificial Intelligence in general, such as the AI4People Ethical Framework, the ethical ramifications that ASAs will have on human relationships as we share not only personal data but our inner most thoughts and feelings, is less explored. We conducted a study with 199 student participants exposed to Sam, an ASA which acts as a personal guide to support the student during their studies. During the interaction, Sam elicits private information, takes decisions for the user and speaks of its own study experiences. Our results indicate that (loss of) autonomy raised the strongest ethical concerns. These results confirm the importance of informed consent, transparency and accountability of ASAs and question the ethics
of false memories and emotion sharing.

The Ethical Acceptability of Artificial Social Agents


In robotics, data acquisition often plays a key part in unknown environment exploration. For example, storing information about the topography of the explored terrain can inform the decision-making process of the robots. Therefore, it is crucial to store these data safely and to make it available quickly to the operators of the robotic system. In a decentralized system like a swarm of robots, this entails several challenges. To address them, we propose RASS, a decentralized risk-aware swarm storage and routing mechanism, which relies exclusively on local information sharing between neighbours to establish storage and routing fitness. We test our system through thorough experiments in a physics-based simulator and obtain convincing reliability, routing speeds, and swarm storage capacity results.

RASS: Risk-Aware Swarm Storage

In multi-objective decision problems, the same state-action pair under different preference weights between the objectives, constitutes different optimal policies. The introduction of changing preference weights interferes with the convergence of the network, and can even stop the network from converging. In this work, we propose a novel experience sampling strategy for multi-objective RL problems, which samples transitions based on the weight and state similarities, to get the sampled experiences close to on-policy. We apply our sampling strategy in multi-objective deep RL algorithms on known benchmark problems, and show that this strongly improves performance.

Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning

The past few years have seen a surge of work on fairness in allocation problems where items must be fairly divided among agents having individual preferences. In comparison, fairness in matching settings with preferences on both sides, that is, where agents have to be matched to other agents, has received much less attention. Moreover, the two-sided matching literature has largely focused on ordinal preferences. We study leximin optimality over stable many-to-one matchings under cardinal preferences. We first investigate matching problems with ranked valuations for which we give efficient algorithms to find the leximin optimal matching over the space of stable matchings. We complement these results by showing that relaxing the ranked valuations condition in any way, makes finding the leximin optimal stable matching intractable.

On Achieving Leximin Fairness and Stability in Many-to-One Matchings


The cost-sharing connection game is a variant of routing games on a network. In this model, given a directed graph with edge costs and capacities, each agent wants to construct a path from a source to a sink with low cost. The cost of each edge is shared by the users based on a cost-sharing function. One of simple cost-sharing functions is defined as the cost divided by the number of users. It models an ideal setting, where no overhead arises when people share things, though it might be quite rare in real life. In this paper, we model more realistic scenarios of cost-sharing connection games by generalizing the cost-sharing function. The arguments do not depend on specific cost-sharing functions and are applicable for a class of all natural cost-sharing scenarios, which include equal divisions with any natural functional overheads. We show that many bounds of the Price of Anarchy and the Price of Stability under sum-cost and max-cost criteria inherit the no-overhead case.

Capacitated Network Design Games on a Generalized Fair Allocation Model

Story sifting (or story recognition) allows for the exploration of events, stories, and patterns that emerge from agent-based simulations. Current approaches to story sifting require users to manually write search queries in a technical language which can be burdensome to users who are unfamiliar with writing code. The goal of this work is to automate and reduce the authoring burden for writing sifting queries. In this paper, we use the event traces of agent-based simulations to create Dynamic Character Networks that track the changing relationship scores between every agent in a simulation. These networks allow for the fortunes between any two agents to be plotted against time as a story arc. By using the time-series comparison technique Dynamic Time Warping, similarity scores between story arcs from the simulation and a user’s query arc can be calculated. Events corresponding to the story arc that best matches the input arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without the need to author a search query. These components are implemented in our experimental prototype Arc Sift which we present in this poster. We also present the results of a user study that supports our expectation that Arc Sift is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events produced by an agent-based simulation.

Automated Story Sifting Using Story Arcs


We consider the allocation of limited resources to heterogeneous customers who arrive in an online fashion. We would like to allocate the resources “fairly”, so that no group of customers is marginalized in terms of their overall service rate. We study whether this is possible to do so in an online fashion, and if so, what a good online allocation policy is.
We model this problem using online bipartite matching under stationary arrivals, a fundamental model in the literature typically studied under the objective of maximizing the total number of customers served. We instead study the objective of maximizing the minimum service rate across all groups, and propose two notions of fairness: long-run and short-run.
For these fairness objectives, we analyze how competitive online algorithms can be, in comparison to offline algorithms which know the sequence of demands in advance. For long-run fairness, we propose two online heuristics (Sampling and Pooling) which establish asymptotic optimality in different regimes (no specialized supplies, no rare demand types, or imbalanced supply/demand). By contrast, outside all of these regimes, we show that the competitive ratio of online algorithms is between 0.632 and 0.732. For short-run fairness, we show for complete bipartite graphs that the competitive ratio of online algorithms is between 0.863 and 0.942; we also derive a probabilistic rejection algorithm which is asymptotically optimal in the total demand.
Depending on the overall scarcity of resources, either our Sampling or Pooling heuristics could be desirable. The most difficult situation for online allocation occurs when the total supply is just enough to serve the total demand, in which case an organization could try to make allocations offline instead. 
We simulate our algorithms on a public ride-hailing dataset, which both demonstrates the efficacy of our heuristics and validates our managerial insights.

Group-level Fairness Maximization in Online Bipartite Matching

Conventional multi-agent reinforcement learning (MARL) methods suffer from low sample efficiency and poor local optimum in complex tasks. We propose a novel framework of Learning to Advise and Learning from Advice (LALA) to enhance MARL, which consists of a centralized advisor, MARL agents and discriminators. The advisor is realized through a spatiotemporal dual graph convolutional network (DualGCN) to give advice to agents from a global perspective via fusing decision information, resolving spatial conflicts, and maintaining temporal continuity. A policy-level generative adversarial learning method is used to advise each agent to learn from advice. Each discriminator trained can distinguish between the policies of the advisor and an agent. Leveraging the discriminator’s judgment, each agent learns to match with the advised policy in addition to learning by its own exploration, which accelerates learning and enhances policy performance. Additionally, an advisor boosting method which incorporates the relevant evaluation made by the discriminators into the training of DualGCN is proposed to further help improve MARL agents. We validate our methods in cooperative navigation tasks. Experimental results demonstrate that our method outperforms baseline methods in terms of both learning efficiency and policy efficacy.


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