Wolfram Willuhn

Bio: Wolfram Willuhn is an academic researcher. The author has contributed to research in topics: Artificial life & Population. The author has an hindex of 2, co-authored 2 publications receiving 62 citations.

Artificial Life Applied to Adaptive Information Agents

Abstract: We propose a model, inspired by recent artificial life theory, applied to the problem of retrieving information from a large, distributed collection of documents such as the World Wide Web. A population of agents is evolved under density dependent selection for the task of locating information for the user. The energy necessary for survival is obtained from both environment and user in exchange for appropriate information. By competing for relevant documents, the agents robustly adapt to their information environment and are allocated to efficiently exploit shared resources. We illustrate the roles played by document locality, adaptive search strategies, and relevance feedback, in the information gathering process.

An Endogenous Fitness Paradigm for Adaptive Information Agents

Abstract: We propose a model, inspired by recent artificial life theory, applied to the problem of retrieving information from a large collection of documents such as the World Wide Web. A population of agents is evolved under density dependent selection for the task of locating information for the user. The energy necessary for survival is obtained from both environment and user in exchange for relevant information. By competing for energy, the agents robustly adapt to their environment and are allocated to efficiently exploit their shared resources.

Adaptive Retrieval Agents: Internalizing Local Contextand Scaling up to the Web

Abstract: This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents. These InfoSpiders browse networked information environments on-line in search of pages relevant to the user, by traversing hyperlinks in an autonomous and intelligent fashion. Each agent adapts to the spatial and temporal regularities of its local context thanks to a combination of machine learning techniques inspired by ecological models: evolutionary adaptation with local selection, reinforcement learning and selective query expansion by internalization of environmental signals, and optional relevance feedback. We evaluate the feasibility and performance of these methods in three domains: a general class of artificial graph environments, a controlled subset of the Web, and (preliminarly) the full Web. Our results suggest that InfoSpiders could take advantage of the starting points provided by search engines, based on global word statistics, and then use linkage topology to guide their search on-line. We show how this approach can complement the current state of the art, especially with respect to the scalability challenge.

Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW

Abstract: Amalthaea is an evolving, multi-agent ecosystem for personalized filtering, discovery, and monitoring of information sites. Amalthaea's primary application domain is the World Wide Web and its main purpose is to assist its users in finding interesting information. Two different categories of agents are introduced in the system: filtering agents that model and monitor the interests of the user and discovery agents that model the information sources.A market-like ecosystem where the agents evolve, compete, and collaborate is presented: agents that are useful to the user or other agents reproduce, while low-performing agents are destroyed. Results from various experiments with different system configurations and varying ratios of user interests versus agents in the system are presented. Finally issues like fine-tuning the initial parameters of the system and establishing and maintaining equilibria in the ecosystem are discussed.

ARACHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery.

Abstract: ARACHNID is a distributed algorithm for information discovery in large, dynamic, distributed environments such as the World Wide Web. The approach is based on a distributed , adaptive population of intelligent agents making local decisions. The behavior of the algorithm is analyzed using a simpliied model of the Web environment. This analysis highlights an interesting feature of the Web environment that bodes well for ARACH-NID's search methods. The performance of the algorithm is illustrated using both simulated environments and preliminary experiments in which prototype agents search real Web environments. Interactions are also discussed between unsupervised learning by individual agents and evolution at the population level, along with the role played in both by user relevance feedback.

Life cycle modeling of news events using aging theory

Abstract: In this paper, an adaptive news event detection method is proposed. We consider a news event as a life form and propose an aging theory to model its life span. A news event becomes popular with a burst of news reports, and it fades away with time. We incorporate the proposed aging theory into the traditional single-pass clustering algorithm to model life spans of news events. Experiment results show that the proposed method has fairly good performance for both long-running and short-term events compared to other approaches.

PVA: A Self-Adaptive Personal View Agent

Abstract: In this paper, we present PVA, an adaptive personal view information agent system for tracking, learning and managing user interests in Internet documents. PVA consists of three parts: a proxy, personal view constructor, and personal view maintainer. The proxy logs the user's activities and extracts the user's interests without user intervention. The personal view constructor mines user interests and maps them to a class hierarchy (i.e., personal view). The personal view maintainer synchronizes user interests and the personal view periodically. When user interests change, in PVA, not only the contents, but also the structure of the user profile are modified to adapt to the changes. In addition, PVA considers the aging problem of user interests. The experimental results show that modulating the structure of the user profile increases the accuracy of a personalization system.