TL;DR: Three optimisation algorithms namely: Dijkstra, A∗, and Genetic algorithms that are used to find multi-criteria paths in construction sites based on transportation and safety-related cost are compared and critically analysed.

TL;DR: This poster presents a poster presenting a probabilistic procedure for estimating the response of the immune system to chemotherapy-like injuries to treat central nervous system injuries.

TL;DR: This paper presents example cases from architectural practice to illustrate the use of robot motion-planning techniques for wheelchair accessibility analysis and illustrates and discusses how to analyze virtual simulations of the detailed behavior of a designed artifact in order to assess its use by intended users.

TL;DR: This paper uses unsupervised Kernel methods to identify the optimal cases to instantiate a case base and compares the efficiencies of the Kernel models measured as Mean Absolute Percentage Error on an oceanographic problem.

TL;DR: All the elements required by the method to successfully extract and verify design knowledge from design databases are described.

TL;DR: An industrial neural network based crowd monitoring system for surveillance at underground station platforms is presented and very promising results were obtained in terms of estimation accuracy and real-time response capability to alert the operators automatically.

TL;DR: In this paper, the authors address the problem of dynamic structuring of manufacturing systems based on the decomposition of manufacturing objectives and the allocation of tasks to autonomous building blocks in a dynamic environment.

TL;DR: A novel immunized reinforcement adaptive learning mechanism employing a behavior-based knowledge and the on-line adapting capabilities of the immune system is proposed and applied to an intelligent mobile robot and results validate several significant characteristics of the immunized architecture.

TL;DR: An enhanced version of PTS, Evolutionary Parallel Tabu Search, is proposed that performs reproduction operators on sub-neighbourhoods directing the search towards more promising areas of the search space and results show that the EPTS outperforms the PTS and Particle Swarm Optimisation algorithms.

TL;DR: Graph-based induction can effectively be applied to the extraction of typical patterns from DNA sequence data and organochlorine compound data from which are to be generated classification rules, and that GBI also works as a feature construction component for other machine learning tools.

TL;DR: An emerging industry standard, ifcXML, is adopted as the knowledge representation format, thereby reducing the effort that is necessary to build a knowledge base and the mechanisms that use information in the knowledge base for question understanding are explored.

TL;DR: The performance of PEmap in this study agrees extremely well with that of FAM, and was employed as a binary classifier for prediction of flashover occurrence in single compartment fire.

TL;DR: This paper introduced a staged design evaluation model as a general yet powerful model of design decision-making process, and developed a methodology for estimation of design intent (MEDI) as a reasoning method.

TL;DR: An identification method for nonlinear models realized in the form of implicit rule-based fuzzy-neural networks (FNN) using the improved complex method to guarantee both global optimization and local convergence.

TL;DR: The use of Reinforcement Learning to the computation of time-optimal anti-swing control of a ship unloader using a multilayer perceptron neural network as a value function approximator is described.

TL;DR: Simulation results show that maximum neuron model can achieve to obtain better solutions than other methods for some kinds of problems in VRPs and TSPs.

TL;DR: The paper shows how by developing a system in conjunction with practising designers, the development process can be steered towards providing a solution which is relatively simple and easy to use and which enables the designers to mobilise their expertise while providing them with a tool which enhances their ability to search for good solutions.

TL;DR: A functional concept ontology is proposed which provides a rich vocabulary representing functions together with clear definitions grounded on behavior that enables the automatic identification system to make the search in the functional space tractable and to screen out meaningless interpretations.

TL;DR: The ANN-based simulations were able to fairly capture the underlying relationship between jobs' machine sequences and their resulting average flowtimes, which proves that ANNs are a viable tool for stochastic simulation metamodeling.

TL;DR: It is demonstrated that the ability to learn greatly enhances agents' negotiation power, and speeds up the rate of convergence between agents in the MASCOT system.

TL;DR: A customer-oriented approach to customer requirements elicitation and evaluation is proposed and investigated in this study, which concerns both breadth and depth perspectives of customer requirements acquisition as well as customer and marketing analysis.