Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge

Nordsieck, Richard; Heider, Michael; Angerer, Alfred; Hähner, Jörg

doi:10.5220/0007953204060413

Cited by 4 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the different variations proposed since its first introduction by Holland, learning classifier systems have witnessed some improvement and real-world applications in recent years [36][37][38][39][40][41]. One of the most main-stream and widely applied learning classifier systems is the extended classifier system [42].…”

Section: Learning Classifier Systemsmentioning

confidence: 99%

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Nantogma

Ran

2021

Symmetry

View full text Add to dashboard Cite

Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of the learning classifier system and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.

show abstract

Section: Learning Classifier Systemsmentioning

confidence: 99%

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Nantogma

Ran

2021

Symmetry

View full text Add to dashboard Cite

show abstract

“…CAROL is developed in the context of a research project with the aim to optimize the parameterization process of fused deposition modelling by studying environmental influences on the process (Nordsieck et al, 2019). To this end, we extract rules based on operator interactions (Nordsieck et al, 2021) and combine them with learning systems (Heider et al, 2020) which requires an accurate dataset.…”

Section: Case Studymentioning

confidence: 99%

CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling

Wiedholz

Heider

Nordsieck

et al. 2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

Self Cite

View full text Add to dashboard Cite

Planning the right grasp pose and motion into it has been a problem in the robotic community for more than 20 years. This paper presents a model-based approach for a Pick action of a robot that increases the automation of FDM based additive manufacturing by removing a produced object from the build plate. We treat grasp pose planning, motion planning and simulation-based verification as separate components to allow a high exchangeability. When testing a variety of different object geometries, feasible grasps and motions were obtained for all objects. We also found that the computation time is highly dependent on the random seed, leading us to employ a system of budgeted runs for which we report the estimated success probability and expected running time. Within the budget, some objects never found feasible picks. Thus, we rotated these objects by 90 • which lead to a substantial improvement in success probabilities.

show abstract

Interactive Knowledge-Guided Learning

Nordsieck

Hähner

2020

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

View full text Add to dashboard Cite

Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge

Cited by 4 publications

References 25 publications

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling

Interactive Knowledge-Guided Learning

Contact Info

Product

Resources

About