Distributed Agent-Based Online Auction System

This paper concerns the design and development of a distributed agent-based online system for English auctions. The proposed system is composed of two parts: an Agent-based Auction Server and a Web-based Graphical User Interface. The first part of our work brought about the advantages introduced by the multi-agent systems technology to the high-level of abstraction, modularity and performance of the server architecture and its implementation. On the server side, bids submitted by auction participants are handled by a hierarchical organization of agents that can be efficiently distributed on a computer network. This approach avoids the bottlenecks of bid processing that might occur during periods of heavy bidding, like for example snipping. We present experimental results that show a significant improvement of the server throughput compared with the architecture where a single auction manager agent is used for coordinating the participants for each active auction that is registered with the server. The second part of our work involved analysis of external functionalities, implementation and usability of a prototype online auction system that incorporates the Agent-based Auction Server. Our solution is outlined in terms of information flow management and its relation to the functionalities of the system. The main outcome of this part of the work is a clean specification of the information exchanges between the agent and non-agent software components of the system. Special attention is also given to the interoperability, understood here as successful integration of the different data communication protocols and software technologies that we employed for the implementation of the system

Computational Mechanism Design: A Call to Arms

Game theory has developed powerful tools for analyzing decision making in systems with multiple autonomous actors. These tools, when tailored to computational settings, provide a foundation for building multiagent software systems. This tailoring gives rise to the field of computational mechanism design, which applies economic principles to computer systems design

Real-Time Bidding by Reinforcement Learning in Display Advertising

The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks.Comment: WSDM 201

Optimal Scheduling of Energy Storage Using A New Priority-Based Smart Grid Control Method

This paper presents a method to optimally use an energy storage system (such as a battery) on a microgrid with load and photovoltaic generation. The purpose of the method is to employ the photovoltaic generation and energy storage systems to reduce the main grid bill, which includes an energy cost and a power peak cost. The method predicts the loads and generation power of each day, and then searches for an optimal storage behavior plan for the energy storage system according to these predictions. However, this plan is not followed in an open-loop control structure as in previous publications, but provided to a real-time decision algorithm, which also considers real power measures. This algorithm considers a series of device priorities in addition to the storage plan, which makes it robust enough to comply with unpredicted situations. The whole proposed method is implemented on a real-hardware test bench, with its different steps being distributed between a personal computer and a programmable logic controller according to their time scale. When compared to a different state-of-the-art method, the proposed method is concluded to better adjust the energy storage system usage to the photovoltaic generation and general consumption.Unión Europea ID 100205Unión Europea ID 26937