David Lillis

ProbFuse: a probabilistic approach to data fusion

Data fusion is the combination of the results of independent searches on a document collection into one single output result set. It has been shown in the past that this can greatly improve retrieval effectiveness over that of the individual results.This paper presents probFuse, a probabilistic approach to data fusion. ProbFuse assumes that the performance of the individual input systems on a number of training queries is indicative of their future performance. The fused result set is based on probabilities of relevance calculated during this training process. Retrieval experiments using data from the TREC ad hoc collection demonstrate that probFuse achieves results superior to that of the popular CombMNZ fusion algorithm.

SoSAA: a framework for integrating components & agents

Modern computing systems require powerful software frameworks to ease their development and manage their complexity. These issues are addressed within both Component-Based Software Engineering and Agent-Oriented Software Engineering, although few integrated solutions exist. This paper discusses a novel integration strategy, which builds upon both paradigms to address their shortcomings while leveraging their different characteristics to define a complete software framework.

Probability-based fusion of information retrieval result sets

Information Retrieval (IR) forms the basis of many information management tasks. Information management itself has become an extremely important area as the amount of electronically available information increases dramatically. There are numerous methods of performing the IR task both by utilising different techniques and through using different representations of the information available to us. It has been shown that some algorithms outperform others on certain tasks. Combining the results produced by different algorithms has resulted in superior retrieval performance and this has become an important research area. This paper introduces a probability-based fusion technique probFuse that shows initial promise in addressing this question. It also compares probFuse with the common CombMNZ data fusion technique.

Estimating probabilities for effective data fusion

Data Fusion is the combination of a number of independent search results, relating to the same document collection, into a single result to be presented to the user. A number of probabilistic data fusion models have been shown to be effective in empirical studies. These typically attempt to estimate the probability that particular documents will be relevant, based on training data. However, little attempt has been made to gauge how the accuracy of these estimations affect fusion performance. The focus of this paper is twofold: firstly, that accurate estimation of the probability of relevance results in effective data fusion; and secondly, that an effective approximation of this probability can be made based on less training data that has previously been employed. This is based on the observation that the distribution of relevant documents follows a similar pattern in most high-quality result sets. Curve fitting suggests that this can be modelled by a simple function that is less complex than other models that have been proposed. The use of existing IR evaluation metrics is proposed as a substitution for probability calculations. Mean Average Precision is used to demonstrate the effectiveness of this approach, with evaluation results demonstrating competitive performance when compared with related algorithms with more onerous requirements for training data.

Extending probabilistic data fusion using sliding windows

Recent developments in the field of data fusion have seen a focus on techniques that use training queries to estimate the probability that various documents are relevant to a given query and use that information to assign scores to those documents on which they are subsequently ranked. This paper introduces SlideFuse, which builds on these techniques, introducing a sliding window in order to compensate for situations where little relevance information is available to aid in the estimation of probabilities. SlideFuse is shown to perform favourably in comparison with CombMNZ, ProbFuse and SegFuse. CombMNZ is the standard baseline technique against which data fusion algorithms are compared whereas ProbFuse and SegFuse represent the state-of-the-art for probabilistic data fusion methods.

An agent-based approach to component management

This paper details the implementation of a software framework that aids the development of distributed and self-configurable software systems. This framework is an instance of a novel integration strategy called SoSAA (SOcially Situated Agent Architecture), which combines Component-Based Software Engineering and Agent-Oriented Software Engineering, drawing its inspiration from hybrid agent control architectures. The framework defines a complete construction process by enhancing a simple component-based framework with reasoning and self-awareness capabilities through a standardized interface. The capabilities of the resulting framework are demonstrated through its application to a non-trivial Multi Agent System (MAS). The system in question is a pre-existing Information Retrieval (IR) system that has not previously taken advantage of CBSE principles. In this paper we contrast these two systems so as to highlight the benefits of using this new hybrid approach. We also outline how component-based elements may be integrated into the Agent Factory agent-oriented application framework.

Dublin Bogtrotters: Agent Herders

This paper describes an entry to the Multi-Agent Programming Contest 2008. The approach employs the pre-existing Agent Factory framework and extends this framework in line with experience gained from its use within the robotics domain.

Space-time diagram generation for profiling multi agent systems

Advances in Agent Oriented Software Engineering have focused on the provision of frameworks and toolkits to aid in the creation of Multi Agent Systems (MASs). However, despite the need to address the inherent complexity of such systems, little progress has been made in the development of tools to allow for the debugging and understanding of their inner workings. This paper introduces a novel performance analysis system, named AgentSpotter, which facilitates such analysis. AgentSpotter was developed by mapping conventional profiling concepts to the domain of MASs. We outline its integration into the Agent Factory multi agent framework.

Evaluation of a Conversation Management Toolkit for Multi Agent Programming

The Agent Conversation Reasoning Engine (ACRE) is intended to aid agent developers to improve the management and reliability of agent communication. To evaluate its effectiveness, a problem scenario was created that could be used to compare code written with and without the use of ACRE by groups of test subjects. This paper describes the requirements that the evaluation scenario was intended to meet and how these motivated the design of the problem. Two experiments were conducted with two separate sets of students and their solutions were analysed using a combination of simple objective metrics and subjective analysis. The analysis suggested that ACRE by default prevents some common problems arising that would limit the reliability and extensibility of conversation-handling code. As ACRE has to date been integrated only with the Agent Factory multi agent framework, it was necessary to verify that the problems identified are not unique to that platform. Thus a comparison was made with best practice communication code written for the Jason platform, in order to demonstrate the wider applicability of a system such as ACRE.

Practical development of hybrid intelligent agent systems with SoSAA

The development of intelligent Multi Agent Systems (MAS) is a non-trivial task. While much past research has focused on high-level activities such as co-ordination and negotiation, the development of tools and strategies to address the lower-level concerns of such systems is a more recent focus. SoSAA (Socially Situated Agent Architecture) is a strategy for the integration of high-level MASs on one hand with component-based systems on the other. Under the SoSAA strategy, a component-based system is used to provide the lower-level implementation of agent tasks and capabilities, allowing for the agent layer to concentrate on high-level intelligent co-ordination and organisation. This paper provides a practical perspective on how SoSAA can be used in the development of intelligent MASs, illustrating this by demonstrating how it can be used to manage backchannel transport services.