A knowledge based expert system for moulded part design

In today's competitive market many consumer products are designed with complex curved shapes to meet customers' demands for styling and ergonomics. These styled products are commonly manufactured using moulding processes because they can produce a wide range of freeform shapes at relatively low cost. However, although injection moulding and metal casting allow a great deal of design freedom they also make significant demands on the designer to ensure that parts are designed with due regard for manufacturability. This paper describes a knowledge based moulding advisor that has been developed to provide design for moulding advice to designers during the design process. The main contributions of the research are the development of a hierarchical knowledge representation to allow moulding advice to be generated at different levels of detail and the integration of the expert system with a geometric part description extracted from a Computer Aided Design (CAD) solid model. A demonstrator for the manufacturing advisor has been implemented using the expert system shell CLIPS and integrated with CAD using feature recognition. The moulding advisor is able to generate tailored design for moulding advice for a range of manufacturing processes and materials and evaluate the manufacturability of a designed part at the feature level. The paper provides a case study for a simple moulded test part

CAES: A Model of an RBR-CBR Course Advisory Expert System

Academic student advising is a gargantuan task that places heavy demand on the time, emotions and mental resources of the academic advisor. It is also a mission critical and very delicate task that must be handled with impeccable expertise and precision else the future of the intended student beneficiary may be jeopardized due to poor advising. One integral aspect of student academic advising is course registration, where students make decisions on the choice of courses to take in specific semesters based on their current academic standing. In this paper, we give the description of the design, implementation and trial evaluation of the Course Advisory Expert System (CAES) which is a hybrid of a rule based reasoning (RBR) and case based reasoning (CBR). The RBR component was implemented using JESS. The result of the trial experiment revealed that the system has high performance/user satisfaction rating from the sample expert population conducted

Use of Automated Trading Systems on Commodity Markets

Diplomová práca sa zameriava na využite automatizácie obchodovania s komoditami pomocou automatických obchodných systémov – expert advisors. Práca popisuje teoretické základy komoditných trhov a princípy obchodovania, technickú analýzy trhu, návrh stratégie a jej implementáciu ako automatický obchodný systém. V závere sú zhodnotené dosiahnuté výsledky.Focus of master's thesis is usability of automated trading of commodities with automated trading systems – expert advisors. Thesis describe theoretical background of commodity markets, trading principles, technical analysis of market, design and implementation of strategy as expert advisor. In conclusion, results are analyzed.

ICADS: A cooperative decision making model with CLIPS experts

A cooperative decision making model is described which is comprised of six concurrently executing domain experts coordinated by a blackboard control expert. The focus application field is architectural design, and the domain experts represent consultants in the area of daylighting, noise control, structural support, cost estimating, space planning, and climate responsiveness. Both the domain experts and the blackboard were implemented as production systems, using an enhanced version of the basic CLIPS package. Acting in unison as an Expert Design Advisor, the domain and control experts react to the evolving design solution progressively developed by the user in a 2-D CAD drawing environment. A Geometry Interpreter maps each drawing action taken by the user to real world objects, such as spaces, walls, windows, and doors. These objects, endowed with geometric and nongeometric attributes, are stored as frames in a semantic network. Object descriptions are derived partly from the geometry of the drawing environment and partly from knowledge bases containing prototypical, generalized information about the building type and site conditions under consideration

The application of scripts to deadlock avoidance

We describe the prototype of an expert system software advisor for the lock manager of a database system. The software advisor, called EAGLE (Expert Advisor for Granting Locks Effectively), is intended to become an embedded expert system within a database management system. EAGLE maintains a record of lock request and lock status within a database management system as an application processes transactions. Eag uses this dynamic lock data to avoid the granting of locks which could lead to a future deadlock. The sequence of lock requests and lock grantings is held as a script(s). EAGLE uses its collected record of lock request sequence to match against stereotypical lock event sequence (script base) and to learn to avoid such sequences in future. As EAGLE gains experience of lock event sequences leading to deadlock it recognises patterns which have led to deadlock, an avoids granting locks which would repeat a previous deadlock-inducing sequence of locks, thereby reducing the occurrence of deadlock. EAGLE treats the deadlock problem as a plan recognition issue rather than a problem resolution issue. We describe the general design of EAGLE, present some results from the EAGLE prototype implementation and discuss planned enhancements to EAGLE

The load shedding advisor: An example of a crisis-response expert system

A Prolog-based prototype expert system is described that was implemented by the Network Operations Branch of the NASA Goddard Space Flight Center. The purpose of the prototype was to test whether a small, inexpensive computer system could be used to host a load shedding advisor, a system which would monitor major physical environment parameters in a computer facility, then recommend appropriate operator reponses whenever a serious condition was detected. The resulting prototype performed significantly to efficiency gains achieved by replacing a purely rule-based design methodology with a hybrid approach that combined procedural, entity-relationship, and rule-based methods

Web-based expert systems:benefits and challenges

Convergence of technologies in the Internet and the field of expert systems have offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-advisor - for e-business strategy development, Fish-Expert - for fish disease diagnosis, and IMIS - to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives. © 2004 Elsevier B.V. All rights reserved

Designinig Coordination among Human and Software Agents

The goal of this paper is to propose a new methodology for designing coordination between human angents and software agents and, ultimately, among software agents. The methodology is based on two key ideas. The first is that coordination should be designed in steps, according to a precise software engineering methodology, and starting from the specification of early requirements. The second is that coordination should be modeled as dependency between actors. Two actors may depend on one another because they want to achieve goals, acquire resources or execute a plan. The methodology used is based on Tropos, an agent oriented software engineering methodology presented in earlier papers. The methodology is presented with the help of a case study

Intelligence student advising system - an implementation using object-oriented C++

This paper present an approach for developing a consistent student course-advising system for undergraduate students using knowledge-based technology. A prototype system has been implemented in object-oriented technique using C++. The prototype system was designed for undergraduate Computing students. The prototype is able to give consultation and advice on some important aspect of student advising problems. Knowledgeable behaviour was produced where the ‘expert’ and ‘knowledge’ is stored separately from the inference engine. Object-oriented programming technique was found to enhance the development of the system

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines