Cooperation in Industrial Systems

ARCHON is an ongoing ESPRIT II project (P-2256) which is approximately half way through its five year duration. It is concerned with defining and applying techniques from the area of Distributed Artificial Intelligence to the development of real-size industrial applications. Such techniques enable multiple problem solvers (e.g. expert systems, databases and conventional numerical software systems) to communicate and cooperate with each other to improve both their individual problem solving behavior and the behavior of the community as a whole. This paper outlines the niche of ARCHON in the Distributed AI world and provides an overview of the philosophy and architecture of our approach the essence of which is to be both general (applicable to the domain of industrial process control) and powerful enough to handle real-world problems

Learning Transformation in the Human and Natural Resources Economics course through the GPT Chat: A Review”

In the world of education, we are led to carry out learning that is easy to understand and carried out by many developments in the field of technology. Where the purpose of the research is to explain Learning Transformation in the Human and Natural Resources Economics course through the GPT Chat. In the Human and Natural Resources Economics course, the use of GPT Chat can provide convenience for students in carrying out the learning transformation process. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. In the world of education, we are led to carry out learning that is easy to understand and carried out by many developments in the field of technology. One of the learning transformations is to use AI as a technology that can help humans achieve greater progress and open up new opportunities for innovation and success in various fields. Especially in human resource and natural resource economics courses, digital-based learning transformation using GPT chat can simplify and access the information needed in the learning process

Análise de Risco: estado da arte da metodologia Hazop generalizada, aplicações e perspectivas na indústria de processos

Introduction: The Hazard and Operability Study is considered a feasible tool to assess risks, where complex technologies, require new strategies to guarantee efficiency, safety, and quality of products. Objective: To perform a Hazop publications review, to establish the state of the art, current procedures and perspectives in the pharmaceutical industry. Method: Hazop methodology and improvements to satisfy actual needs were structured. Subsequently, its application and integration with other risk tools, and experts systems, were analyzed to define the current approach and future perspectives. Results: The review allowed the understanding where models, simulations and specialized software offered adequate support to assess risk in current complex processes. In addition, an efficient definition of causes and consequences depends of expert systems, where simulations acquire experience through the creation of databases, reducing the need of specific process knowledge, which is a typical limitation of the conventional Hazop methodology. Conclusions: A review of the Hazop stateof- the-art highlighted the importance to assess risks within the process industry. However, the use of new technologies designed to meet regulatory affairs to guarantee safety and quality principles would require the ongoing improvement of the Hazop methodology, restricting the dependence of specialists, and increasing the use of expert systems.Introducción: El Estudio de Riesgos y Operabilidad se considera una herramienta factible para evaluar los riesgos, cuando las tecnologías complejas requieren nuevas estrategias para garantizar la eficiencia, la seguridad y la calidad de los productos. Objetivo: realizar una revisión de las publicaciones de Hazop, para establecer el estado del arte, los procedimientos actuales y las perspectivas en la industria farmacéutica. Método: fue estructurada la metodología Hazop y las mejoras para satisfacer las necesidades reales. Posteriormente, se analizó su aplicación e integración con otras herramientas de riesgo y sistemas de expertos para definir el enfoque actual y las perspectivas futuras. Resultados: la revisión permitió comprender dónde los modelos, las simulaciones y el software especializado ofrecían el soporte adecuado para evaluar el riesgo en los procesos complejos actuales. Además, una definición eficiente de causas y consecuencias depende de los sistemas expertos, donde las simulaciones adquieren experiencia a través de la creación de bases de datos, lo que reduce la necesidad de un conocimiento específico del proceso, que es una limitación típica de la metodología convencional Hazop. Conclusiones: una revisión del estado del arte de Hazop resaltó la importancia de evaluar los riesgos dentro de la industria de procesos. Sin embargo, el uso de nuevas tecnologías diseñadas para cumplir con los asuntos regulatorios para garantizar los principios de seguridad y calidad requeriría la mejora continua de la metodología Hazop, restringiendo la dependencia de especialistas y aumentando el uso de sistemas expertos.Título PT: Análise de Risco: estado da arte da metodologia Hazop generalizada, aplicações e perspectivas na indústria de processos Introdução: O Estudo de Perigos e Operabilidade (Hazop) é considerado uma ferramenta para avaliação de riscos, na qual tecnologias complexas exigem novas estratégias para garantir a eficiência, a segurança e a qualidade dos produtos. Objetivo: Realizar uma revisão de publicações do Hazop, para estabelecer o estado da arte, os procedimentos e as suas perspectivas na indústria farmacêutica. Método: O procedimento Hazop e suas adequações para satisfazer as necessidades atuais foram estruturados. Posteriormente, aplicações e integração com outras ferramentas de risco e sistemas expertos foram analisadas para definir a abordagem atual e perspectivas futuras. Resultados: A revisão permitiu a compreensão de que modelos, simulações e software especializado oferecem suporte para avaliar riscos em processos complexos. Adicionalmente, a correta definição de causas e consequências depende do uso de sistemas expertos, cujas simulações adquirem experiência através da criação de bancos de dados, reduzindo a necessidade de conhecimento específico do processo, que é uma limitação da metodologia Hazop convencional. Conclusões: A revisão do estado da arte do Hazop destacou a importância de avaliar riscos dentro da indústria de processos. No entanto, novas tecnologias utilizadas para atender quesitos regulatórios de segurança e qualidade precisam da melhoria contínua da metodologia Hazop, reduzindo a dependência de especialista por meio do uso de sistemas especializados

Learning structured task related abstractions

As robots and autonomous agents are to assist people with more tasks in various domains they need the ability to quickly gain contextual awareness in unseen environments and learn new tasks. Current state of the art methods rely predominantly on statistical learning techniques which tend to overfit to sensory signals and often fail to extract structured task related abstractions. The obtained environment and task models are typically represented as black box objects that cannot be easily updated or inspected and provide limited generalisation capabilities. We address the aforementioned shortcomings of current methods by explicitly studying the problem of learning structured task related abstractions. In particular, we are interested in extracting symbolic representations of the environment from sensory signals and encoding the task to be executed as a computer program. We consider the standard problem of learning to solve a task by mapping sensory signals to actions and propose the decomposition of such a mapping into two stages: i) perceiving symbols from sensory data and ii) using a program to manipulate those symbols in order to make decisions. This thesis studies the bidirectional interactions between the agent’s capabilities to perceive symbols and the programs it can execute in order to solve a task. In the first part of the thesis we demonstrate that access to a programmatic description of the task provides a strong inductive bias which facilitates the learning of structured task related representations of the environment. In order to do so, we first consider a collaborative human-robot interaction setup and propose a framework for Grounding and Learning Instances through Demonstration and Eye tracking (GLIDE) which enables robots to learn symbolic representations of the environment from few demonstrations. In order to relax the constraints on the task encoding program which GLIDE assumes, we introduce the perceptor gradients algorithm and prove that it can be applied with any task encoding program. In the second part of the thesis we investigate the complement problem of inducing task encoding programs assuming that a symbolic representations of the environment is available. Therefore, we propose the p-machine – a novel program induction framework which combines standard enumerative search techniques with a stochastic gradient descent optimiser in order to obtain an efficient program synthesiser. We show that the induction of task encoding programs is applicable to various problems such as learning physics laws, inspecting neural networks and learning in human-robot interaction setups

Advisor - an expert system shell written in Prolog

The purpose of the research reported here is to investigate the requirements of a system which will aid a designer in moving from the functional specification of a new product to a detailed Lcchnical specification of the polymer required, and to develop a system for (his purpose. A very wide range of products can be made from polymers and over 5000 polymers are commercially available. The designer usually interacts with a polymer expert and together they try to specify the polymer properties required. Over 150 parameters are involved. Once values have been assigned to these parameters existing databases can be used to identify a suitable material. Polymer experts are scarcc and there is a need for a system which wilt allow the designer to specify the parameter values dictated by the functional requirements of the design. A number of problems are identified in this area. Some of these are of a terminological and communication nature and arise from the wide range of the application areas and large numbers of parameters to be specified. Others are due to the knowledge engineering problems in fonnalising the knowledge of the polymer expert. An investigation of these problems lead to the specification of a system which combines a flexible quasi-English naLural language with a rule based paradigm. A prototype system called Advisor is built. Advisor has the unique ability of allowing the expert to create not only the rules but also the vocabulary with which these rules are constructed and with which the designer builds up a description of the application. Thus all the vocabulary used within the system is familiar to both the expert and the designer. The underlying language for the implementation of Advisor is Arity Prolog. The prototype validates the basic design decisions. Analysis of it’s performance and suggestions for further refinements and improvements are given. The prototype system which is being puL into use by a commercial plastic design company is currently being evaluated by polymer design experts in Aachcn in Germany