New Zealand

Bagdat Teltayev (March 27th 2018). Fatigue Destruction of Asphalt Concrete Pavement: Self- Organization and Mechanical Interpretation, Modified Asphalt, Jose Luis Rivera-Armenta and Beatriz Adriana Salazar-Cruz, IntechOpen, DOI: 10.5772/intechopen.75536.

Ümit Ulusoy, Mehmet Serdar Güzel and Erkan Bostanci (April 22nd 2020). A Q-Learning-Based Approach for Simple and Multi-Agent Systems, Multi Agent Systems - Strategies and Applications, Ricardo López - Ruiz, IntechOpen, DOI: 10.5772/intechopen.88484.

Javier B. Cabrera, Manuel F. Veiga, Diego X. Morales and Ricardo Medina (November 27th 2019). Reducing Power Losses in Smart Grids with Cooperative Game Theory, Advanced Communication and Control Methods for Future Smartgrids, Taha Selim Ustun, IntechOpen, DOI: 10.5772/intechopen.88568. 

Games are an increasingly useful tool for training and testing learning algorithms. Recent examples include GANs, AlphaZero and the AlphaStar league. However, multi-agent learning can be extremely difficult to predict and control. Learning dynamics can yield chaotic behavior even in simple games. In this paper, we present basic mechanism design tools for constructing games with predictable and controllable dynamics. We present a robust framework for dimensionality reduction arguments in large network games.

AAMAS 2020

Robust Self-Organization in Games: Symmetries, Conservation Laws and Dimensionality Reduction

non-cooperative games: theory and analysis

self-organization

multi-agent learning

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). 

The open-source Unity platform, where agents can be trained using hierarchical or non-hierarchical reinforcement learning, supports the use of games and simulations as environments for multiple- agent interactions. In this demonstration, we present hierarchical and non-hierarchical multi-agent interactions based on Unity reinforcement learning, specifically, hierarchical reinforcement learning that sets different levels of agent’s observations to achieve the goal. We created four multi-agent scenarios in the Unity environment, namely, Crawler, Tennis, Banana Collector, and Soccer, to test the interaction performances of hierarchical and non- hierarchical reinforcement learning. The simulation-interaction performances show that hierarchical reinforcement learning can be applied to multi-agent environments and can compete with agents trained via non-hierarchical reinforcement learning. 


Peer Reviewing in Participatory Guarantee Systems

Axiomatic approaches are an appealing method for designing fair algorithms, as they provide formal structure for reasoning about and rationalizing individual decisions. However, to make these algorithms useful in practice, their axioms must appropriately capture social norms. We explore this tension between fairness axioms and socially acceptable decisions in the context of cooperative game theory. We use two crowdsourced experiments to study people's impartial reward divisions in cooperative games, focusing on games that systematically vary the values of the single-player coalitions. Our results show that people select rewards that are remarkably consistent, but place much more emphasis on the single-player coalitions than the Shapley value does. Further, their reward divisions violate both the null player and additivity axioms, but support weaker axioms. We argue for a more general methodology of testing axioms against experimental data, retaining some of the conceptual simplicity of the axiomatic approach while still using people’s opinions to drive the design of fair algorithms.

Testing Axioms Against Human Reward Divisions in Cooperative Games

Recent years have seen various designs of strategyproof mechanisms in the facility location game and the obnoxious facility game, by considering the facility as a point. In this paper, we extend that point to be an interval and study a novel activity scheduling game to schedule an activity in the time domain [0, 1] based on all agents’ time reports. The activity lasts for a time period of d with 0 <=d<=1, and each agent i wants his private time 𝑡𝑖 to be within the activity duration [y,y+d] or at least as close as possible. Thus his cost is the time difference between his time t_i and the activity duration [y, y+d]. The social cost is the summation of all agents’ costs. Our objective is to choose the activity starting time 𝑦 so that the mechanisms are strategyproof (truthful) and efficient. We design a mechanism outputting an optimal solution and prove it is group strategyproof. For minimizing the maximum cost, we also design a strategyproof mechanism with approximation ratio 2. In the obnoxious activity scheduling game, each agent prefers his conflict time t_i to be far away from the activity duration [y,y+d]. We respectively design deterministic and randomized group strategyproof mechanisms with provable approximation ratios and also show the lower bounds. Besides, for extension, we consider the cost/utility as the characteristic function and find group strategyproof mechanisms for minimizing the social cost and maximizing the social utility.

Strategyproof Mechanisms for Activity Scheduling

From skipped exercise classes to last-minute cancellation of dentist appointments, underutilization of reserved resources abounds. Likely reasons include uncertainty about the future, further exacerbated by present bias. In this paper, we unite resource allocation and commitment devices through the design of contingent payment mechanisms, and propose the two-bid penalty-bidding mechanism. This extends an earlier mechanism proposed by Ma et al. (2019), assigning the resources based on willingness to accept a no-show penalty, while also allowing each participant to increase her own penalty in order to counter present bias. We establish a simple dominant strategy equilibrium, regardless of an agent's level of present bias or degree of "sophistication". Via simulations, we show that the proposed mechanism substantially improves utilization and achieves higher welfare and better equity in comparison with mechanisms used in practice and mechanisms that optimize welfare in the absence of present bias.

Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present Bias

Many websites that recommend various services use crowdsourcing to collect reviews and rankings. These rankings, usually concerning a subset of all the offered alternatives, are then aggregated. Motivated by such scenarios, we axiomatise a family of positional scoring rules for profiles of possibly incomplete individual preferences. Many opportunities arise for the agents to manipulate the outcome in this setting. They may lie in order to obtain a better result by: (i) switching the order of a ranked pair of alternatives, (ii) omitting some of their truthful preferences, or (iii) reporting more preferences than the ones they truthfully hold. After formalising these new concepts, we characterise all positional scoring rules that are immune to manipulation.

Strategic Manipulation with Incomplete Preferences: Possibilities and Impossibilities for Positional Scoring Rules

Vaccination is a standard public health intervention for controlling the spread of epidemics. However, the supply of vaccines is typically limited, and therefore, their deployment needs to be optimized. Further, vaccines are produced over time, so the strategies have to be temporal. We study the problem \prob{} of designing vaccination strategies, within available budget constraints, to minimize the spread of an outbreak. This is a challenging stochastic optimization problem. We design a bicriteria approximation algorithm, which combines a linear programming based rounding, along with the sample average approximation technique. Our approach also provides the empirical approximation factor for the problem instance, relative to the optimum. We find that the approximation factor is significantly better than the worst case bound, and, in practice, is a small constant factor. Further, our method shows significantly better performance than all prior heuristics for this problem. With additional pruning techniques, we are able to scale our algorithm to networks with millions of edges.

Designing Effective and Practical Interventions to Contain Epidemics

We propose a game-theoretic approach to generalizing the classical Schelling model. At the core of our model are two features that did not receive much attention before. First, we allow multiple individuals to occupy the same location. Second, each individual's choice of location is influenced by their social network neighbors that also choose the same location. In addition, an individual's choice is influenced by others in the adjacent locations in a network-structured way, which captures the main spirit of the classical Schelling model and its numerous extensions. Our solution concept is a stable configuration represented as a pure-strategy Nash equilibrium (PSNE). We show that even for various special cases of the problem, computing or counting PSNE is provably hard. We give algorithms for computing PSNE, including efficient algorithms for several special cases. We highlight some of the attractive features of our model, such as predicting very few PSNE, through experiments.

Schelling Models with Localized Social Influence: A Game-Theoretic Framework

Report-sensitive spot-checking in peer grading systems

We develop a methodology for comparing two or more agent-based models that are developed for the same domain, but may differ in the particular data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase transition boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption.

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