A Multi-Agent System framework to support the decision-making in complex real-world domains

The aim of this work was to develop a framework capable of supporting the decision-making process in complex real-world domains, such as environmental, industrial or medical domains using a Multi-Agent approach with Rule-based Reasoning. The validation of the framework was done in the environmental domain, particularly in the area of river basins

First CLIPS Conference Proceedings, volume 2

The topics of volume 2 of First CLIPS Conference are associated with following applications: quality control; intelligent data bases and networks; Space Station Freedom; Space Shuttle and satellite; user interface; artificial neural systems and fuzzy logic; parallel and distributed processing; enchancements to CLIPS; aerospace; simulation and defense; advisory systems and tutors; and intelligent control

Uses and applications of artificial intelligence in manufacturing

The purpose of the THESIS is to provide engineers and personnels with a overview of the concepts that underline Artificial Intelligence and Expert Systems. Artificial Intelligence is concerned with the developments of theories and techniques required to provide a computational engine with the abilities to perceive, think and act, in an intelligent manner in a complex environment. Expert system is branch of Artificial Intelligence where the methods of reasoning emulate those of human experts. Artificial Intelligence derives it\u27s power from its ability to represent complex forms of knowledge, some of it common sense, heuristic and symbolic, and the ability to apply the knowledge in searching for solutions. The Thesis will review : The components of an intelligent system, The basics of knowledge representation, Search based problem solving methods, Expert system technologies, Uses and applications of AI in various manufacturing areas like Design, Process Planning, Production Management, Energy Management, Quality Assurance, Manufacturing Simulation, Robotics, Machine Vision etc. Prime objectives of the Thesis are to understand the basic concepts underlying Artificial Intelligence and be able to identify where the technology may be applied in the field of Manufacturing Engineering

A RULE-BASED APPROACH TO ANIMATING MULTI-AGENT ENVIRONMENTS

This dissertation describes ESCAPE (Expert Systems in Computer Animation Production Environments), a multi-agent animation system for building domain-oriented, rule-based visual programming environments. Much recent work in computer graphics has been concerned with producing behavioural animations of artificial life-forms mainly based on algorithmic approaches. This research indicates how, by adding an inference engine and rules that describe such behaviour, traditional computer animation environments can be enhanced. The comparison between using algorithmic approaches and using a rule-based approach for representing multi-agent worlds is not based upon their respective claims to completeness, but rather on the ease with which end users may express their knowledge and control their animations with a minimum of technical knowledge. An environment for the design of computer animations incorporating an expert system approach is described. In addition to direct manipulation of objects on the screen, the environment allows users to describe behavioural rules based upon both the physical and non-physical attributes of objects. These rules can be interpreted to suggest the transition from stage to stage or to automatically produce a longer animation. The output from the system can be integrated into a commercially available 3D modelling and rendering package. Experience indicates that a hybrid environment, mixing algorithmic and rule-based approaches, would be very promising and offer benefits in application areas such as creating realistic background scenes and modelling human beings or animals either singly or in groups. A prototype evaluation system and three different domains are described and illustrated with preliminary animated images

Development of new methodologies for the weight estimation of aircraft structures

The problem of weight estimation in the aerospace industry has been acquiring considerably greater importance in recent years, due to the numerous challenges frequently encountered in the preliminary phases of the design of a new aircraft. This is the stage where it is possible to make design changes without incurring into excessive cost penalties. On the other hand, the knowledge of the design, of the relationships existing between the different variables and their subsequent impact on the final weight of the structure is very limited. As a result, the designer is unable to understand the true effect that individual design decisions will produce on the weight of the structure. In addition to this, new aircraft concepts end up being too conservative, due to the high dependency of current weight estimation methods to historical data and off-the-shelf design solutions. This thesis aims at providing an alternative framework for the weight estimation of aircraft structures at preliminary design stages. By conducting a thorough assessment of current state-of-the-art approaches and tools used in the field, fuzzy logic is presented as an appropriate foundation on which to build an innovative approach to the problem. Different adaptive fuzzy approaches have been used in the development of a methodology which is able to combine an analytical base to the structural design of selected trailing edge components, with substantial knowledge acquisition capabilities for the computation of robust and reliable weight estimates. The final framework allows considerable flexibility in the level of detail of the estimate consistent with the granularity of the input data used. This, combined with an extensive uncertainty analysis through the use of Interval Type-2 fuzzy logic, will provide the designer with the capabilities to understand the impact of error propagation within the model and increase the confidence in the final estimat

A Framework for assessing in silico Toxicity Predictions: Case Studies with selected Pesticides

In the regulatory assessment of chemicals, the use of in silico prediction methods such as (quantitative) structure-activity relationship models ([Q]SARs), is increasingly required or encouraged, in order to increase the efficiency and effectiveness of the risk assessment process, and to minimise the reliance on animal testing. The main question for the assessor concerns the usefulness of the prediction approach, which can be broken down into the practical applicability of the method and the adequacy of the predictions. A framework for assessing and documenting (Q)SAR models and their predictions has been established at the European and international levels. Exactly how the framework is applied in practice will depend on the provisions of the specific legislation and the context in which the non-testing data are being used. This report describes the current framework for documenting (Q)SAR models and their predictions, and discuses how it might be built upon to provide more detailed guidance on the use of (Q)SAR predictions in regulatory decision making. The proposed framework is illustrated by using selected pesticide active compounds as examples.JRC.DG.I.6-Systems toxicolog

Validation and verification of the interconnection of hardware intellectual property blocks for FPGA-based packet processing systems

As networks become more versatile, the computational requirement for supporting additional functionality increases. The increasing demands of these networks can be met by Field Programmable Gate Arrays (FPGA), which are an increasingly popular technology for implementing packet processing systems. The fine-grained parallelism and density of these devices can be exploited to meet the computational requirements and implement complex systems on a single chip. However, the increasing complexity of FPGA-based systems makes them susceptible to errors and difficult to test and debug. To tackle the complexity of modern designs, system-level languages have been developed to provide abstractions suited to the domain of the target system. Unfortunately, the lack of formality in these languages can give rise to errors that are not caught until late in the design cycle. This thesis presents three techniques for verifying and validating FPGA-based packet processing systems described in a system-level description language. First, a type system is applied to the system description language to detect errors before implementation. Second, system-level transaction monitoring is used to observe high-level events on-chip following implementation. Third, the high-level information embodied in the system description language is exploited to allow the system to be automatically instrumented for on-chip monitoring. This thesis demonstrates that these techniques catch errors which are undetected by traditional verification and validation tools. The locations of faults are specified and errors are caught earlier in the design flow, which saves time by reducing synthesis iterations

Reaction Based Grasp Force Assignment

An iterative method is developed by which the contact forces required to apply an arbitrary wrench (six elements of force and moment) to a stably grasped object may be calculated quickly. The assignment of contact forces, given a required object wrench, is accomplished with the use of fuzzy logic. This concept is referred to as the fuzzy logic reactive system (FLRS). The solution is versatile with respect to goals inherent in the rulebase and the input parameters, and is also applicable for an arbitrary number of contacts. The goal presented in this research, to illustrate the concept of the FLRS, is the minimization of the norm of the contact forces using point contacts with friction. The results comparing the contact force assignment for this method and the optimal method proposed by Nakamura are presented. The results show that FLRS will satisfy the object wrench and frictional contacts while achieving near optimal contact force assignment. This method is shown to require significantly fewer floating point operations than the solution calculated using numerical constrained optimization techniques