David Pardoe

AAMAS Conference 2011 Conference Paper

A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations

• David Pardoe
• Peter Stone

Keyword auctions are becoming increasingly important in today's electronic marketplaces. One of their most challenging aspects is the limited amount of information revealed about other advertisers. In this paper, we present a particle filter that can be used to estimate the bids of other advertisers given a periodic ranking of their bids. This particle filter makes use of models of the bidding behavior of other advertisers, and so we also show how such models can be learned from past bidding data. In experiments in the Ad Auction scenario of the Trading Agent Competition, the combination of this particle filter and bidder modeling outperforms all other bid estimation methods tested.

ICML Conference 2010 Conference Paper

Boosting for Regression Transfer

• David Pardoe
• Peter Stone 0001

AAMAS Conference 2010 Conference Paper

TacTex09: A Champion Bidding Agent for Ad Auctions

• David Pardoe
• Doran Chakraborty
• Peter Stone

In the Trading Agent Competition Ad Auctions Game, agentscompete to sell products by bidding to have their ads shownin a search engine's sponsored search results. We report onthe winning agent from the first (2009) competition, TacTex. TacTex operates by estimating the full game state from limited information, using these estimates to make predictions, and then optimizing its actions (daily bids, ads, and spending limits) with respect to these predictions. We present afull description of TacTex along with analysis of its performance in both the competition and controlled experiments.

AAMAS Conference 2007 Conference Paper

Adapting in Agent-Based Markets: A Study from TAC SCM

• David Pardoe
• Peter Stone

An agent attempting to model market conditions may benefit from considering how various combinations of competitor strategies would impact these conditions. We give an illustration using a prediction task faced by our agent for the Supply Chain Management scenerio of the Trading Agent Competition(TAC SCM). We present the learning approach taken, evaluate its effectiveness, and then explore methods of improving predictions through combining multiple sources of data reflecting various combinations of competitor behaviors.

ICAPS Conference 2006 Conference Paper

Predictive Planning for Supply Chain Management

• David Pardoe
• Peter Stone 0001

Supply chains are ubiquitous in the manufacturing of many complex products. Traditionally, supply chains have been created through the intricate interactions of human representatives of the various companies involved. However, recent advances in planning, scheduling, and autonomous agent technologies have sparked an interest, both in academia and in industry, in automating the process. The Trading Agent Competition Supply Chain Management (TAC SCM) scenario provides a unique testbed for studying and prototyping supply chain management agents by providing a competitive environment in which independently created agents can be tested against each other over the course of many simulations. This paper presents the features of TAC SCM from a planning and scheduling perspective and introduces TacTex-05, the champion agent from the 2005 competition. TacTex-05 takes a predictive approach to its many planning and scheduling decisions by estimating future resource availability and constraints. This paper focuses on these aspects of the agent and isolates their impact with controlled empirical tests.

AAAI Conference 2006 Conference Paper

TacTex-05: A Champion Supply Chain Management Agent

• David Pardoe

Supply chains are ubiquitous in the manufacturing of many complex products. Traditionally, supply chains have been created through the interactions of human representatives of the companies involved, but advances in autonomous agent technologies have sparked an interest in automating the process. The Trading Agent Competition Supply Chain Management (TAC SCM) scenario provides a unique testbed for studying supply chain management agents. This paper introduces TacTex-05 (the champion agent from the 2005 competition), describes its constituent intelligent components, and examines the success of the complete agent through analysis of competition results and controlled experiments.

ICML Conference 2003 Conference Paper

Learning Predictive State Representations

• Satinder Singh 0001
• Michael L. Littman
• Nicholas K. Jong
• David Pardoe
• Peter Stone 0001