13,740 research outputs found
WhiteDolphin: A TAC travel agent.
No full textIn this paper, we detail our WhiteDolphin agent that was designed for the Trading Agent Competition (TAC) Travel game. Specifically, we employed the multi-layered IKB framework to design our strategy, and describe the intricate cogs involved at the different layers in this complex decision-making process. We focus, in particular, on WhiteDolphin’s strategic behaviour when bidding in the different types of auctions involved in the game, and how the information and knowledge required to support the complex decisions made is gathered and inferred respectively. Finally, we empirically analyse our agent by considering its performance in the 2006 competition where it ranked third
SouthamptonTAC: An adaptive autonomous trading agent
No full textSoftware agents are increasingly being used to represent humans in on-line auctions. Such agents have the advantages of being able to systematically monitor a wide variety of auctions and then make rapid decisions about what bids to place in what auctions. They can do this continuously and repetitively without losing concentration. Moreover, in complex multiple auction settings, agents may need to modify their behavior in one auction depending on what is happening in another. To provide a means of evaluating and comparing (benchmarking) research methods in this area, the Trading Agent Competition (TAC) was established. This competition involves a number of agents bidding against one another in a number of related auctions (operating different protocols) to purchase travel packages for customers. Against this background, this artcle describes the design, implementation and evaluation of our adaptive autonomous trading agent, SouthamptonTAC, one of the most successful participants in TAC 2002
Blazin – A TAC Classic Agent Solutions and Strategies
No full textTAC Agent Trading Game – Amit Kothari, Brian Ferguson 1 Introduction........................................................................................................................................... 3 1.1 Trading Agent Competition.........................................................................................................
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HarTAC– The Harvard TAC SCM'03 Agent
No full textThe Trading Agent Competition (TAC) is an annual event in which teams from around the world compete in a given scenario concerning the trading agent problem. This paper describes some of the key features and strategies employed by HarTAC, the Harvard University TAC SCM 2003 agent. HarTAC was built to be a moderately aggressive, production-oriented agent, combining a semi-conservative component purchasing strategy with a highly aggressive cycle-based sell side.Engineering and Applied SciencesAuthor's Origina
Mertacor 2009 Demystified: Strategies and Guidelines for Market Design in TAC
No full textCurrent global economy involves highly interconnected markets competing with each other for market share and profit. The recent global financial crisis has revived research interest in this competition and has brought out the need for novel, efficient rules. TAC Market Design tournament is one of the first efforts in studying the interaction between opponent stock exchanges. In this paper, we describe our entrant for 2009, Mertacor, and reason about the importance of proper pricing in this global setting
Designing a successful trading agent using fuzzy techniques
No full textSoftware agents are increasingly being used to represent humans in on-line auctions. Such agents have the advantages of being able to systematically monitor a wide variety of auctions and then make rapid decisions about what bids to place in what auctions. They can do this continuously and repetitively without losing concentration. To provide a means of evaluating and comparing (benchmarking) research methods in this area the Trading Agent Competition (TAC) was established. This competition involves a number of agents bidding against one another in a number of related auctions (operating different protocols) to purchase travel packages for customers. Against this background, this paper describes the design, implementation and evaluation of Southampton- TAC, one of the most successful participants in both the Second and the Third International Competitions. Our agent uses fuzzy techniques at the heart of its decision making: to make bidding decisions in the face of uncertainty, to make predictions about the likely outcomes of auctions, and to alter the agent’s bidding strategy in response to the prevailing market conditions. Keywords: intelligent agents, fuzzy set, fuzzy reasoning, on-line auctions, trading agent competition
Evolution of a supply chain management game for the trading agent competition
No full textTAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt
Stratum: A methodology for designing heuristic agent negotiation strategies.
No full textAutomated negotiation is a powerful (and sometimes essential) means for allocating resources among self-interested autonomous software agents. A key problem in building negotiating agents is the design of the negotiation strategy, which is used by an agent to decide its negotiation behaviour. In complex domains, there is no single, obvious optimal strategy. This has led to much work on designing heuristic strategies, where agent designers usually rely on intuition and experience. In this paper, we introduce STRATUM, a methodology for designing strategies for negotiating agents. The methodology provides a disciplined approach to analysing the negotiation environment and designing strategies in light of agent capabilities, and acts as a bridge between theoretical studies of automated negotiation and the software engineering of negotiation applications. We illustrate the application of the methodology by characterising some strategies for the Trading Agent Competition and for argumentation-based negotiation
Flexible Decision Control in an Autonomous Trading Agent
No full textAn autonomous trading agent is a complex piece of software that must operate in a competitive economic environment and support a research agenda. We describe the structure of decision processes in the MinneTAC trading agent, focusing on the use of evaluators – configurable, composable modules for data analysis and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports sales and procurement decisions, and how those decision processes can be modified in useful ways by changing evaluator configurations. To put this work in context, we also report on results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM).autonomous trading agent;decision processes
Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges
No full textWe show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents
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