Flexible Decision Control in an Autonomous Trading Agent

An 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

Flexible Decision Control in an Autonomous Trading Agent

An 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)

A Multi-Agent Energy Trading Competition

The energy sector will undergo fundamental changes over the next ten years. Prices for fossil energy resources are continuously increasing, there is an urgent need to reduce CO2 emissions, and the United States and European Union are strongly motivated to become more independent from foreign energy imports. These factors will lead to installation of large numbers of distributed renewable energy generators, which are often intermittent in nature. This trend conflicts with the current power grid control infrastructure and strategies, where a few centralized control centers manage a limited number of large power plants such that their output meets the energy demands in real time. As the proportion of distributed and intermittent generation capacity increases, this task becomes much harder, especially as the local and regional distribution grids where renewable energy generators are usually installed are currently virtually unmanaged, lack real time metering and are not built to cope with power flow inversions (yet). All this is about to change, and so the control strategies must be adapted accordingly. While the hierarchical command-and-control approach served well in a world with a few large scale generation facilities and many small consumers, a more flexible, decentralized, and self-organizing control infrastructure will have to be developed that can be actively managed to balance both the large grid as a whole, as well as the many lower voltage sub-grids. We propose a competitive simulation test bed to stimulate research and development of electronic agents that help manage these tasks. Participants in the competition will develop intelligent agents that are responsible to level energy supply from generators with energy demand from consumers. The competition is designed to closely model reality by bootstrapping the simulation environment with real historic load, generation, and weather data. The simulation environment will provide a low-risk platform that combines simulated markets and real-world data to develop solutions that can be applied to help building the self-organizing intelligent energy grid of the future

AAAI 2008 Workshop Reports

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy

Hierarchical reinforcement learning for trading agents

Autonomous software agents, the use of which has increased due to the recent growth in computer power, have considerably improved electronic commerce processes by facilitating automated trading actions between the market participants (sellers, brokers and buyers). The rapidly changing market environments pose challenges to the performance of such agents, which are generally developed for specific market settings. To this end, this thesis is concerned with designing agents that can gradually adapt to variable, dynamic and uncertain markets and that are able to reuse the acquired trading skills in new markets. This thesis proposes the use of reinforcement learning techniques to develop adaptive trading agents and puts forward a novel software architecture based on the semi-Markov decision process and on an innovative knowledge transfer framework. To evaluate my approach, the developed trading agents are tested in internationally well-known market simulations and their behaviours when buying or/and selling in the retail and wholesale markets are analysed. The proposed approach has been shown to improve the adaptation of the trading agent in a specific market as well as to enable the portability of the its knowledge in new markets

Information Sharing in Multi-Tier Supply Chains - Moving Beyond the Dyads

As international competition increased towards the end of last century, companies realized the importance of collaborating and sharing information with suppliers and customers to remain competitive. By sharing information such as forecasts of future demand with partners in the supply chain, it was possible to lower inventory holding costs and increase the service level to customers, and thereby increase the competitiveness of the involved companies. Previous research has proposed several benefits related to information sharing in supply chains. Suggested benefits include, for instance, better planning and scheduling of production lines, improved allocation and utilization of resources for transportation and warehousing, and reduced inventory levels and tied-up capital. However, it appears that few companies have been able to implement and benefit from information sharing. Several researchers conclude that information sharing in the supply chain is limited. Particularly, it seems as if companies have not been able to benefit from sharing information across multiple tiers in the supply chain. The lack of information sharing across multiple tiers is a challenge which is important to address considering that companies continue to struggle with problems related to the, so called, bullwhip effect. A supply chain which suffers from the bullwhip effect can experience distorted demand information as it is shared upstream in the chain. Such variations in demand information can lead to incorrect production planning and thereby alternately high inventory levels and increased costs for overtime and rush orders. Against this background, several questions arise: For what reasons do companies refrain from sharing information across multiple tiers despite the fact that literature suggest that it is beneficial? Further, are there any companies that have implemented information sharing across multiple tiers and, if so, what are the documented benefits? Searching the answers to these questions reveals a gap in literature in that the majority of previous research studies have focused on dyadic relationship (i.e. supplier-buyer) instead of multi-tier supply chains (e.g. supplier-manufacturer-customer). The purpose with this dissertation is therefore to move beyond the dyads and explore information sharing in the supply chain, and investigate opportunities and challenges involved with sharing information across multiple tiers. The purpose is addressed in three separate but connected studies. Following a pilot study, a systematic literature review is conducted to establish current knowledge in the research area. Thereafter, two empirical studies are conducted: a case study which maps an entire supply chain where data is collected from multiple tiers; and a Delphi study including a panel of experts who share their insights through multiple questionnaires. The findings indicate that companies, for different reasons, refrain from sharing information across multiple supply chain tiers. One reason is the many challenges involved with implementing information sharing across multiple supply chain tiers. The major challenges include lack of trust between companies; lack of information quality; difficulties to share risks and benefits; lack of business processes; and the lack of a dominant player who can initiate change in the supply chain. Many companies are also preoccupied with internal issues and lack the ability to engage in information sharing across the supply chain. Another reason, from the perspective of contingency theory, seems to be that information sharing across multiple tiers is only beneficial in few, particular contexts. Such contexts relate to planned changes in the supply chain, for example in relation to new product introductions when future demand is uncertain. Findings also suggest a negative inter-relation between important and feasible contexts. In other words, in cases where it is possible to implement information sharing it is perceived to be less valuable and in cases where it is more valuable it is more difficult to implement. The findings further suggest that companies focus their information sharing with supply chain partners that represent high intensity of interdependence. Interdependence theory can thus help to explain why companies mostly share information with dyadic, strategic partners where the partners represent a large percentage of each other’s portfolio and turnover. Moving beyond the dyads, the intensity of interdependence is reduced as firms are embedded in many networks and often have multiple suppliers and customers. The willingness to engage in multi-tier information sharing is therefore reduced. This dissertation, which is one of the first to study information sharing in the extended supply chain, indicates that information sharing across multiple tiers is a rare phenomenon in industry. The dissertation also points out that several aspects must be considered to be able to implement and benefit from information sharing across multiple tiers. One of the contributions of the dissertation is a conceptual framework which can be used to guide future research and also function as decision support for companies to address and implement multi-tier information sharing

Agile Market Engineering: Bridging the gap between business concepts and running markets

The agile market engineering process model (AMEP) is built on the insight, that market design and development is a wicked problem. Electronic markets are too complex to be completely designed upfront. Instead, AMEP tries to bridge the gap between theoretic market design and practical electronic market platform development using an agile, iterative approach that relies on early customer feedback and continuous improvement. The AMEP model is complemented by several supporting software artifacts

Desenvolupament d'un agent autònom competitiu per a la TAC-SCM

En aquest projecte hem desenvolupat una metodologia per a avaluar agents que vulguin competir en la TACSCM. També realitzem un estudi sobre multi-agents en la competició i programem diversos agents per fer-ne comparacions entre si, i extreure'n el màxim d'informació útil possible, de cara a la TACSCM