New Zealand


In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. Despite the effectiveness of these mandates, the only way to eradicate the disease is to achieve herd immunity through vaccination. In order to increase the accessibility of vaccines, states such as Virginia have funded mobile vaccination centers to distribute vaccines across the state. When choosing where to place these sites, there are two important factors to take into account: accessibility and equity. We develop efficient algorithms with theoretical guarantees on both of these aspects. Finally, we run computational experiments on real world data to show the efficacy of our methods.

AAMAS 2022

Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response

computational epidemiology

approximation algorithms

facility location

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 (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).

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).


Classification and clustering are prevalent to recognize the identity and the community of nodes in graphs. Several studies analyzed graph structure to build graph neural network (GNN) models for reducing the accuracy of node classification and clustering recently. The existing defense usually modified the structure of models and adopted countermeasure training to enhance the robustness of the node classification and clustering. However, these defense works are model-oriented and not robust. To alleviate the problem, this paper first proposed a robust node classification metric based on residual entropy. More concrete, the accuracy of node classification is proven robustly by maximizing the residual entropy of the nodes theoretically. Then two graph generative algorithms are proposed to resist against two kinds of GNN-based attacks, the untargeted attack and the targeted attack, respectively. Finally, experimental analysis show that the proposed algorithms outperform the existing defense works under five classic datasets.

Residual Entropy-based Graph Generative Algorithms


Agent-based models (ABMs) highlight the importance of simulation validation, such as qualitative face validation and quantitative empirical validation. In particular, we focused on quantitative validation by adjusting simulation input parameters of the ABM. This study introduces an automatic calibration framework that combines the suggested dynamic and heterogeneous calibration methods. Specifically, the dynamic calibration fits the simulation results to the real-world data by automatically capturing suitable simulation time to adjust the simulation parameters, which continuously regulates the simulation diverging from the real world. Conversely, the heterogeneous calibration fits the distributional discrepancy between individuals in simulation and the real world by adjusting agent related parameters cluster-wisely.

Automatic calibration framework of agent-based models for dynamic and heterogeneous parameters


We present a logic of responsibility. Extending stit theory with epistemic, doxastic, deontic, and intentional modalities, we provide logic-based characterizations of several modes of responsibility.

A Stit Logic of Responsibility

**Read paper on the following link:**

https://ifaamas.org/Proceedings/aamas2022/pdfs/p1607.pdf

**Abstract:**



A Graph Neural Network Reasoner for Game Description Language

Existing approaches to coalition formation often assume that requirements associated with tasks are precisely specified by the human operator. However, prior work has demonstrated that humans, while extremely adept at solving complex problems, struggle to explicitly state their solution strategy. Further, existing approaches often ignore the fact that experts may utilize different, but equally-valid, solutions (i.e., heterogeneous strategies} to the same problem. In this work, we propose a two-part framework to address these challenges. First, we tackle the challenge of inferring implicit strategies directly from expert demonstrations of coalition formation. To this end, we model and infer such heterogeneous strategies as capability-based requirements associated with each task. Next, we propose a method capable of adaptively selecting one of the inferred strategies that best suits the target team without requiring additional training. Specifically, we formulate and solve a constrained optimization problem that simultaneously selects the most appropriate strategy given the target team's capabilities, and allocates its constituents into appropriate coalitions. We evaluate our approach against several baselines, including some that resemble existing approaches, using detailed numerical simulations, StarCraft II battles, and a multi-robot emergency-response scenario. Our results indicate that our framework consistently outperforms all baselines in terms of requirement satisfaction, resource utilization, and task success rates.

Resource-Aware Adaptation of Heterogeneous Strategies for Coalition Formation

Diversity in behaviors is instrumental for robust team performance in many multiagent tasks which require agents to coordinate. Unfortunately, exhaustive search through the agents' behavior spaces is often intractable.
This paper introduces Behavior Exploration for Heterogeneous Teams (BEHT), a multi-level learning framework that enables agents to progressively explore regions of the behavior space that promote team coordination on diverse goals.
By combining diversity search to maximize agent-specific rewards and evolutionary optimization to maximize the team-based fitness, our method effectively filters regions of the behavior space that are conducive to agent coordination.
We demonstrate the diverse behaviors and synergies that are method allows agents to learn on a multiagent exploration problem.

Behavior Exploration and Team Balancing for Heterogeneous Multiagent Coordination

Efficient contact tracing and isolation is an effective strategy to control epidemics, as seen in the Ebola epidemic and COVID-19 pandemic. An important consideration in contact tracing is the budget on the number of individuals asked to quarantine---the budget is limited for socioeconomic reasons (e.g., having a limited number of contact tracers). Here, we present a Markov Decision Process (MDP) framework to formulate the problem of using contact tracing to reduce the size of an outbreak while limiting the number of people quarantined. We formulate each step of the MDP as a combinatorial problem, MinExposed, which we demonstrate is NP-Hard. Next, we develop two approximation algorithms, one based on rounding the solutions of a linear program and another (greedy algorithm) based on choosing nodes with a high (weighted) degree. A key feature of the greedy algorithm is that it does not need complete information of the underlying social contact network, making it implementable in practice. Using simulations over realistic networks, we show how the algorithms can help in {\em bending} the epidemic curve with a limited number of isolated
individuals.

Theoretical Models and Preliminary Results for Contact Tracing and Isolation


Continuous Action-space Interactive Reinforcement learning (CAIR) is a continuous action-space interactive reinforcement learning algorithm that can out-preform state-of-the-art reinforcement learning algorithms early on in training. We test CAIR in two simulated robotics environments with intuitive and easy to design heuristic teachers.

Environment Guided Interactive Reinforcement Learning: Learning from Binary Feedback in High-Dimensional Robot Task Environments

Recently, text world games have been proposed to enable artifi- cial agents to understand and reason about real-world scenarios. These text-based games are challenging for artificial agents, as it requires an understanding of and interaction using natural language in a partially observable environment. Agents observe the environment via textual descriptions designed to be challenging enough for even human players. Past approaches have not paid enough attention to the language understanding capability of the proposed agents. Typically, these approaches train from scratch, an agent that learns both textual representations and the gameplay online during training using a temporal loss function. Given the sample-inefficiency of RL approaches, it is inefficient to learn rich enough textual representations to be able to understand and reason using the textual observation in such a complicated game environment setting. In this paper, we improve the semantic understanding of the agent by proposing a simple RL with LM framework where we use transformer-based language models with Deep RL models. We perform a detailed study of our framework to demonstrate how our model outperforms all existing agents on the popular game, Zork1, to achieve a score of 44.7, which is 1.6 higher than the state-of-the- art model. Overall, our proposed approach outperforms 4 games out of the 14 text-based games, while performing comparable to the state-of-the-art models on the remaining games.

Pre-trained Language Models as Prior Knowledge for Playing Text-based Games


Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achieve this goal, demonstrating success in a series of challenging problems. However, whilst these advances are significant, the vast majority of focus has been on the self-play paradigm. This often results in a coordination problem, caused by agents learning to make use of arbitrary conventions when playing with themselves. This means that even the strongest self-play agents may have very low cross-play with other agents, including other initializations of the same algorithm. In this paper we propose to solve this problem by adapting agent strategies on the fly, using a posterior belief over the other agents' strategy. Concretely, we consider the problem of selecting a strategy from a finite set of previously trained agents, to play with an unknown partner. We propose an extension of the classic statistical technique, Gibbs sampling, to update beliefs about other agents and obtain close to optimal ad-hoc performance. Despite its simplicity, our method is able to achieve strong cross-play with unseen partners in the challenging card game of Hanabi, achieving successful ad-hoc coordination without knowledge of the partner's strategy a priori.


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Residual Entropy-based Graph Generative Algorithms

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Residual Entropy-based Graph Generative Algorithms

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Optimizing Multiple Simultaneous Objectives for Voting and Facility Location

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