David Pardoe

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We introduce the rst algorithm for learning predictive state representations (PSRs), which are a way of representing the state of a controlled dynamical system. The state representation in a PSR is a vector of predictions of tests, where tests are sequences of actions and observations said to be true if and only if all the observations occur given that all(More)
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(More)
The goal of transfer learning is to improve the learning of a new target concept given knowledge of related source concept(s). We introduce the first boosting-based algorithms for transfer learning that apply to regression tasks. First, we describe two existing classification transfer algorithms, ExpBoost and TrAdaBoost, and show how they can be modified(More)
Supply chains are a current, challenging problem for agent-based electronic commerce. Motivated by the Trading Agent Competition Supply Chain Management (TAC SCM) scenario, we consider an individual supply chain agent as having three major subtasks: acquiring supplies, selling products, and managing its local manufacturing process. In this paper, we focus(More)
This paper introduces TacTex-03, an agent designed to participate in the Trading Agent Competition Supply Chain Management Scenario (TAC SCM). As specified by this scenario, TacTex-03 acts as a simulated computer manufacturer in charge of buying components such as chips and motherboards, manufacturing different types of computers, and selling them to(More)
Supply Chain Management involves planning for the procurement of materials, assembly of finished products from these materials, and distribution of products to customers. The Trading Agent Competition Supply Chain Management scenario (TAC SCM) provides a competitive benchmarking environment for developing and testing agent-based solutions to supply chain(More)
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(More)
In the Trading Agent Competition Ad Auctions Game, agents compete to sell products by bidding to have their ads shown in a search engine’s sponsored search results. We report on the 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,(More)
Mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, these assumptions may not hold, making bidder behavior difficult to model and complicating the design process. To address this issue, we propose a different approach to mechanism design. Instead of relying on(More)
Scaling of the quantified dispositional parameters of xenobiotics from animals to man is of interest from the standpoint of toxicology (e.g., poisoning and risk assessment). Scaling is also important from the standpoint of therapeutics because it represents a strategy for predicting first-use-in-human doses in clinical trials of investigational new drugs.(More)