All Outputs (1974)

Human Behavious Modelling for Discrete Event and Agent Based Simulation: A Case Study
Conference Proceeding
Majid, M., Aickelin, U., & Siebers, P. Human Behavious Modelling for Discrete Event and Agent Based Simulation: A Case Study.

This study is about the comparison of simulation techniques between Discrete Event Simulation (DES) and Agent Based Simulation (ABS). DES is one of the best-known types of simulation techniques in Operational Research. Recently, there has been an e... Read More about Human Behavious Modelling for Discrete Event and Agent Based Simulation: A Case Study.

The Application of a Dendric Cell Algorithm to a Robotic Classifier
Conference Proceeding
Oates, R., Greensmith, J., Aickelin, U., Garibaldi, J., & Kendall, G. The Application of a Dendric Cell Algorithm to a Robotic Classifier.

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an invest... Read More about The Application of a Dendric Cell Algorithm to a Robotic Classifier.

A Multi-Agent Simulation of Retail Management Practices
Conference Proceeding
Siebers, P., Aickelin, U., Celia, H., & Clegg, C. A Multi-Agent Simulation of Retail Management Practices.

We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fac... Read More about A Multi-Agent Simulation of Retail Management Practices.

Using Intelligent Agents to Understand Management Practices and Retail Productivity
Conference Proceeding
Siebers, P., Aickelin, U., Celia, H., & Clegg, C. Using Intelligent Agents to Understand Management Practices and Retail Productivity.

Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relati... Read More about Using Intelligent Agents to Understand Management Practices and Retail Productivity.

Biological Inspiration for Artificial Immune Systems
Conference Proceeding
Twycross, J., & Aickelin, U. Biological Inspiration for Artificial Immune Systems.

Artificial immune systems (AISs) to date have generally been inspired by naive biological metaphors. This has limited the effectiveness of these systems. In this position paper two ways in which AISs could be made more biologically realistic are disc... Read More about Biological Inspiration for Artificial Immune Systems.

Motif detection inspired by immune memory
Journal Article
Wilson, W., Birkin, P., & Aickelin, U. Motif detection inspired by immune memory. Lecture Notes in Artificial Intelligence, 4628, https://doi.org/10.1007/978-3-540-73922-7_24

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unkn... Read More about Motif detection inspired by immune memory.

Detecting Motifs in System Call Sequences
Conference Proceeding
Wilson, W., Feyereisl, J., & Aickelin, U. Detecting Motifs in System Call Sequences.

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs which... Read More about Detecting Motifs in System Call Sequences.

Belief and bounded rationality
Journal Article
Jago, M. (2006). Belief and bounded rationality

Imagine the possibilities: information without overload
Journal Article
Jago, M. (2006). Imagine the possibilities: information without overload

Information is often modelled as a set of relevant possibilities, treated as logically possible worlds. However, this has the unintuitive consequence that the logical consequences of an agent's information cannot be informative for that agent. There... Read More about Imagine the possibilities: information without overload.

Simulation Optimization of the Crossdock Door Assignmnet Problem
Conference Proceeding
Aickelin, U., & Adewunmi, A. (2006). Simulation Optimization of the Crossdock Door Assignmnet Problem.

The purpose of this report is to present the Crossdock Door Assignment Problem, which involves assigning destinations to outbound dock doors of Crossdock centres such that travel distance by material handling equipment is minimized. We propose a two... Read More about Simulation Optimization of the Crossdock Door Assignmnet Problem.

Improved Squeaky Wheel Optimisation for Driver Scheduling
Conference Proceeding
Aickelin, U., Burke, E., & Li, J. Improved Squeaky Wheel Optimisation for Driver Scheduling.

This paper presents a technique called Improved Squeaky Wheel Optimisation (ISWO) for driver scheduling problems. It improves the original Squeaky Wheel Optimisation’s (SWO) effectiveness and execution speed by incorporating two additional steps of S... Read More about Improved Squeaky Wheel Optimisation for Driver Scheduling.

Detecting Botnets Through Log Correlation
Conference Proceeding
Al-Hammadi, Y., & Aickelin, U. Detecting Botnets Through Log Correlation.

Botnets, which consist of thousands of compromised machines, can cause a significant threat to other systems by launching Distributed Denial of Service attacks, keylogging, and backdoors. In response to this threat, new effective techniques are neede... Read More about Detecting Botnets Through Log Correlation.

BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning
Book
Bacardit, J., & Krasnogor, N. (2006). BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning. Computer Science & IT

This technical report briefly describes our recent work in the iterative rule learning approach (IRL) of evolutionary learning/genetics-based machine learning. This approach was initiated by the SIA system. A more recent example is HIDER. Our approac... Read More about BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning.

Dempster-Shafer for Anomaly Detection
Conference Proceeding
Chen, Q., & Aickelin, U. Dempster-Shafer for Anomaly Detection.

In this paper, we implement an anomaly detection system using the Dempster-Shafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We furt... Read More about Dempster-Shafer for Anomaly Detection.

Modelling Immunological Memory
Book Chapter
Garrett, S., Robbins, M., Walker, J., Wilson, W., & Aickelin, U. Modelling Immunological Memory. In D. Flower, & J. Timmis (Eds.), Silico Immunology. Springer

Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an... Read More about Modelling Immunological Memory.

Dendritic Cells for Real-Time Anomaly Detection
Conference Proceeding
Greensmith, J., & Aickelin, U. Dendritic Cells for Real-Time Anomaly Detection.

Dendritic Cells (DCs) are innate immune system cells which have the power to activate or suppress the immune system. The behaviour of human DCs is abstracted to form an algorithm suitable for anomaly detection. We test this algorithm on the real-time... Read More about Dendritic Cells for Real-Time Anomaly Detection.

Articulation and Clarification of the Dendric Cell Algorithm
Conference Proceeding
Greensmith, J., Aickelin, U., & Twycross, J. Articulation and Clarification of the Dendric Cell Algorithm.

The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of compute... Read More about Articulation and Clarification of the Dendric Cell Algorithm.

Dendritic Cells for Anomaly Detection
Conference Proceeding
Greensmith, J., Twycross, J., & Aickelin, U. Dendritic Cells for Anomaly Detection.

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Dan... Read More about Dendritic Cells for Anomaly Detection.

Rule-based and Resource-bounded: A New Look at Epistemic Logic
Conference Proceeding
Jago, M. (2006). Rule-based and Resource-bounded: A New Look at Epistemic Logic.

Syntactic logics do not suffer from the problems of logical omniscience but are often thought to lack interesting properties relating to epistemic notions. By focusing on the case of rule-based agents, I develop a framework for modelling resource-bou... Read More about Rule-based and Resource-bounded: A New Look at Epistemic Logic.

Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling
Book Chapter
Li, J., & Aickelin, U. Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling. In M. Pelikan, K. Sastry, & E. Cantu-Paz (Eds.), Algorithms to Applications (Studies in Computational Intelligence). Springer

Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such su... Read More about Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling.