New Zealand

We propose a market-inspired bidding scheme for the assignment of paper reviews in large academic conferences. The primary contribution is an analysis of the incentives of reviewers during the bidding phases, when reviewers have private costs and some information about the demand for each paper, and want to obtain the best possible k papers for a predetermined k. We show that by assigning budgets to reviewers and a `price&#39; for every paper that is (roughly) inversely proportional to its demand, the best response of a reviewer is to bid sincerely, i.e., on her most favorite papers, and match the budget even when it is not enforced. Finally, we show via extensive simulations on bidding data from real conferences, that our suggested bidding scheme would substantially improve the assignment, under several common assignment algorithms.

AAMAS 2020

A Market-Inspired Bidding Scheme for Peer Review Paper Assignment

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

We propose a market-inspired bidding scheme for the assignment of paper reviews in large academic conferences. The primary contribution is an analysis of the incentives of reviewers during the bidding phases, when reviewers have private costs and some information about the demand for each paper, and want to obtain the best possible k papers for a predetermined k. We show that by assigning budgets to reviewers and a `price' for every paper that is (roughly) inversely proportional to its demand, the best response of a reviewer is to bid sincerely, i.e., on her most favorite papers, and match the budget even when it is not enforced. Finally, we show via extensive simulations on bidding data from real conferences, that our suggested bidding scheme would substantially improve the assignment, under several common assignment algorithms.

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). 

In the capacitated production planning problem, quantities of products need to be determined at consecutive periods within a given time horizon when product demands, costs, and production capacities vary through time. We focus on a general formulation of this problem where each product is produced in one step and setup cost is paid at each period of production. Additionally, products can be anticipated or backordered in respect to the demand period. We propose a computationally efficient decentralized approach based on the spillover effect relating to the accumulation of production costs of each product demand through time. The performance of the spillover algorithm is compared against the state-of-the-art mixed integer programming branch-and-bound solver CPLEX 12.8 considering optimality gap and computational time.

A Decentralized Multi-Agent Coordination Method for Dynamic and Constrained Production Planning

Reinforcement learning has been applied to numerous decision-making problems, but it requires careful specification of a reward function to represent the objective of the problem.
In many problems, it is difficult to represent the objective as a function, and thus it is easier to give expert demonstrations. Inverse reinforcement learning can be applied to such problems because it estimates the reward function from the expert's demonstrations.
Most existing inverse reinforcement learning methods assume that an expert gives demonstrations in a fixed environment, although the expert can provide demonstrations for a specific objective in multiple environments. For example, it is difficult to formulate a suitable reward function to represent driving a car, and the driver can give demonstrations in multiple situations. In such cases, normal practice is to use demonstrations in multiple environments to estimate the expert's reward. Herein, we formulate this problem based on a Bayesian inverse reinforcement learning framework and propose a mini-batch Markov chain Monte Carlo method. An advantage of our method is scalability. Our proposed method is scalable with respect to a number of environments in which expert demonstrations are generated. Experimental results show quantitatively that the proposed method outperforms existing inverse reinforcement learning methods.

Mini-batch Bayesian Inverse Reinforcement Learning for Multiple Dynamics

Levaraging Communication Topologies between Learning Agents in Deep Reinforcements Learning

Geographical clusters of undervaccinated populations have emerged in various parts of the United States in recent years. Public health response involves surveillance and field work, which is very resource-intensive. Given that public health resources are often limited, iden- tifying and rank-ordering critical clusters can help prioritize and allocate scarce resources for surveillance and quick intervention. We quantify the criticality of a cluster as the additional number of infections caused if the cluster is underimmunized. We focus on finding clusters that maximize this measure and develop efficient approximation algorithms for finding critical clusters by exploiting structural properties of the problem. Our methods involve solving a more general problem of maximizing a submodular function on a graph with connectivity constraints. We apply our methods to the state of Minnesota, where we find clusters with significantly higher criticality than those obtained by heuristics used in public health.

Finding Spatial Clusters Susceptible to Epidemic Outbreaks due to Undervaccination

Multi-agent Adversial Inverse Reinforcemetn Learning with Latent Variables

The Price of Anarchy of Self-Selection in Tullock Contests

Narrow the gap between distribution of task goals and virtual goals.

Hindsight Planner

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities.

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarity and instability. In this paper, we first demonstrate that standard softmax-based policy gradient can be prone to poor performance in the presence of even the most benign nonstationarity. By contrast, it is known that the replicator dynamics, a well-studied model from evolutionary game theory, eliminates dominated strategies and exhibits convergence of the time-averaged trajectories to interior Nash equilibria in zero-sum games. Thus, using the replicator dynamics as a foundation, we derive an elegant one-line change to policy gradient methods that simply bypasses the gradient step through the softmax, yielding a new algorithm titled Neural Replicator Dynamics (NeuRD). NeuRD reduces to the exponential weights/Hedge algorithm in the single-state all-actions case. Additionally, NeuRD has formal equivalence to softmax counterfactual regret minimization, which guarantees convergence in the sequential tabular case. Importantly, our algorithm provides a straightforward way of extending the replicator dynamics to the function approximation setting. Empirical results show that NeuRD quickly adapts to nonstationarities, outperforming policy gradient significantly in both tabular and function approximation settings, when evaluated on the standard imperfect information benchmarks of Kuhn Poker, Leduc Poker, and Goofspiel.

Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients

The problem of finding optimal solutions of stochastic functions over continuous domains is common in several real-world applications, such as, e.g., advertisement allocation, dynamic pricing, and power control in wireless networks. The optimization process is customarily performed by selecting input points sequentially and receiving a noisy observation from the function. In this paper, we resort to the Multi-Armed Bandit approach, aiming at optimizing stochastic functions when keeping at a pace the regret (i.e., the loss incurred during the learning process). In particular, we focus on smooth stochastic functions, as it is known that any algorithm suffers from a constant per-round regret when the domain is continuous, and the function does not satisfy any kind of regularity. Our main original contribution is the provision of a general family of algorithms, which, under the mild assumption that stochastic functions are a realization of a Gaussian Process, provides a regret of the order of O(\sqrt{\gamma_T T}), being \gamma_T the maximum information gain and T the time horizon used for the learning process. Furthermore, we design a specific algorithm of our family, called DAGP-UCB, which exploits the structure of GPs to select the next arm to pull more effectively than the previous algorithms available in the state of the art, thus speeding up the learning process. In particular, we show the superior performance of DAGP-UCB in both synthetic and applicative settings, comparing it with the state-of-the-art algorithms.

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