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 investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

As one of the newest members in the field of artificial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classification results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.

The dendritic cell algorithm (DCA) has been applied successfully to a diverse range of applications. These applications are related by the inherent uncertainty associated with sensing the application environment. The DCA has performed well using unfiltered signals from each environment as inputs. In this paper we demonstrate that the DCA has an emergent filtering mechanism caused by the manner in which the cell accumulates its internal variables. Furthermore we demonstrate a relationship between the migration threshold of the cells and the transfer function of the algorithm. A tuning methodology is proposed and a robotic application published previously is revisited using the new tuning technique.

Abstract. 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 investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

We present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is timeconsuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists in automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed.

This work examines the dendritic cell algorithm (DCA) from a mathematical perspective. By representing the signal processing phase of the algorithm using the dot product it is shown that the signal processing element of the DCA is actually a collection of linear classifiers. It is further shown that the decision boundaries of these classifiers have the potentially serious drawback of being parallel, severely limiting the applications for which the existing algorithm can be potentially used on. These ideas are further explored using artificially generated data and a novel visualisation technique that allows an entire population of dendritic cells to be inspected as a single classifier. The paper concludes that the applicability of the DCA to more complex problems is highly limited.

Part 5: Intelligent Control of Assembly SystemsInternational audienceThe ramp-up process of assembly systems has a huge impact on both the productivity of those systems and the quality of the output. In this work we present a new technique for accelerating the ramp-up process by automatically capturing knowledge about a machine and subsequently reusing it to inform an engineer performing ramp-up. This technique relies on a novel process called the Knowledge Object Algorithm. The technique is explained and demonstrated using synthetic data, designed to emulate a typical use case of such a system. The future direction for this work is also outlined and further experiments detailed

Abstract. Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes have been suggested in the literature to correct for these findings. A contribution of this work is to investigate the effects of replacing the classification stage of the DCA (which is known to be flawed) with a traditional machine learning technique. This work goes on to question the merits of those unique properties of the DCA that are yet to be thoroughly analysed. If none of these properties can be found to have a benefit over traditional approaches, then "fixing" the DCA is arguably less efficient than simply creating a new algorithm. This work examines the dynamic filtering property of the DCA and questions the utility of this unique feature for the anomaly detection problem. It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset. It is concluded that there are still unique features of the DCA left to investigate. Areas that may be of benefit to the Artificial Immune Systems community are suggested.