667 research outputs found
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 Knowledge-Based Engineering System Framework for the Development of Electric Machines
The new concept industry 4.0 is a great opportunity to improve the competitiveness in a global market for small-medium size electric machinery companies. The demand for electric motors have increased in the last decade especially due to applications that try to make a full transition from fuel to electricity. These applications encounter the need for tailor-made motors that must meet demanding requirements. Therefore, it is mandatory small-medium companies adopt new technologies offering customized products fulfilling the customers’ requirements according to their investment capacity. Furthermore, simplify their development process as well as to reduce computational time to achieve a feasible design in shorter periods. In addition, find ways to retain know-how that is typically kept within each designer either to retrieve it or transfer it to new designers.
To support the aforementioned issue, a knowledge-based engineering (KBE) system framework for the development of electric machines is devised. The framework is encapsulated in the so-called KBV2-model comprising the standardized macro-level framework for electrical machine and the knowledge base generation process. This thesis describes this model and the integration of KBE applications with current industrial technologies such as Model-Based Systems Engineering (MBSE), Product Lifecycle Management (PLM), multiphysics and analytical design tools. This architecture provides capability to manage and automate tasks in the development process of electric machines.
The author of this work has opted to develop KBE applications following the minimum viable product principle. The KBE system framework herein presented is formalized through the experience and analysis of the development and implementation of the KBE applications. From which a guideline is provided following a sequential process in order to achieve a viable KBE system. To substantiate the process a KBE system is created that supports the development of electric motors for the elevator system industry
Proceedings of the Workshop on Change of Representation and Problem Reformulation
The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning
Cyber-Physical Embedded Systems with Transient Supervisory Command and Control: A Framework for Validating Safety Response in Automated Collision Avoidance Systems
The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness of its design. However, by using both human and machine agency at different levels of automation, or autonomy, the levels of automation have profound implications and affects to the reliability and safety of the CPS. The human-agent and the machine-agent are in a tidal lock of decision-making using both feedforward and feedback information flows in similar processes, where a transient shift within the level of automation when the CPS is operating can have undesired consequences. As CPS systems become more common, and higher levels of autonomy are embedded within them, the relationship between human-agent and machine-agent also becomes more complex, and the testing methodologies for verification and validation of performance and correctness also become more complex and less clear. A framework then is developed to help the practitioner to understand the difficulties and pitfalls of CPS designs and provides guidance to test engineering design of soft computational systems using combinations of modeling, simulation, and prototyping
Advanced flight control system study
A fly by wire flight control system architecture designed for high reliability includes spare sensor and computer elements to permit safe dispatch with failed elements, thereby reducing unscheduled maintenance. A methodology capable of demonstrating that the architecture does achieve the predicted performance characteristics consists of a hierarchy of activities ranging from analytical calculations of system reliability and formal methods of software verification to iron bird testing followed by flight evaluation. Interfacing this architecture to the Lockheed S-3A aircraft for flight test is discussed. This testbed vehicle can be expanded to support flight experiments in advanced aerodynamics, electromechanical actuators, secondary power systems, flight management, new displays, and air traffic control concepts
Technological roadmap on AI planning and scheduling
At the beginning of the new century, Information Technologies had become basic and indispensable
constituents of the production and preparation processes for all kinds of goods and services and
with that are largely influencing both the working and private life of nearly every citizen. This
development will continue and even further grow with the continually increasing use of the Internet
in production, business, science, education, and everyday societal and private undertaking.
Recent years have shown, however, that a dramatic enhancement of software capabilities is required,
when aiming to continuously provide advanced and competitive products and services in all these
fast developing sectors. It includes the development of intelligent systems – systems that are more
autonomous, flexible, and robust than today’s conventional software.
Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has
been developed and matured over the last three decades and has successfully been employed for a
variety of applications in commerce, industry, education, medicine, public transport, defense, and
government.
This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning
and Scheduling. It identifies the most important research, development, and technology transfer
efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in
the short-, medium- and longer-term future.
The roadmap has been developed under the regime of PLANET – the European Network of Excellence
in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating
framework for research, development, and technology transfer in the field of Intelligent Planning and
Scheduling in Europe.
A large number of people have contributed to this document including the members of PLANET non-
European international experts, and a number of independent expert peer reviewers. All of them are
acknowledged in a separate section of this document.
Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing
along the directions pointed out in this roadmap will enable a new generation of intelligent
application systems in a wide variety of industrial, commercial, public, and private sectors
Learning Static Knowledge for AI Planning Domain Models via Plan Traces
Learning is fundamental to autonomous behaviour and from the point of view of Machine Learning, it is the ability of computers to learn without being programmed explicitly. Attaining such capability for learning domain models for Automated Planning (AP) engines is what triggered research into developing automated domain-learning systems. These systems can learn from training data. Until recent research it was believed that working in dynamically changing and unpredictable environments, it was not possible to construct action models a priori. After the research in the last decade, many systems have proved effective in engineering domain models by learning from plan traces. However, these systems require additional planner oriented information such as a partial domain model, initial, goal and/or intermediate states. Hence, a question arises - whether or not we can learn a dynamic domain model, which covers all domain behaviours from real-time action sequence traces only.
The research in this thesis extends an area of the most promising line of work that is connected to work presented in an REF Journal paper. This research aims to enhance the LOCM system and to extend the method of Learning Domain Models for AI Planning Engines via Plan Traces. This method was first published in ICAPS 2009 by Cresswell, McCluskey, and West (Cresswell, 2009). LOCM is unique in that it requires no prior knowledge of the target domain; however, it can produce a dynamic part of a domain model from training. Its main drawback is that it does not produce static knowledge of the domain, and its model lacks certain expressive features. A key aspect of research presented in this thesis is to enhance the technique with the capacity to generate static knowledge. A test and focus for this PhD is to make LOCM able to learn static relationships in a fully automatic way in addition to the dynamic relationships, which LOCM can already learn, using plan traces as input.
We present a novel system - The ASCoL (Automatic Static Constraints Learner) which provides a graphical interface for visual representation and exploits directed graph discovery and analysis technique. It has been designed to discover domain-specific static relations/constraints automatically in order to enhance planning domain models. The ASCoL method has wider applications. Combined with LOCM, ASCoL can be a useful tool to produce benchmark domains for automated planning engines. It is also useful as a debugging tool for improving existing domain models. We have evaluated ASCoL on fifteen different IPC domains and on different types of goal-oriented and random-walk plans as input training data and it has been shown to be effective
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A Generic Library of Problem Solving Methods for Scheduling Applications
In this thesis we propose a generic library of scheduling problem-solving methods. As a first approximation, scheduling can be defined as an assignment of jobs and activities to resources and time ranges in accordance with a number of constraints and requirements. In some cases optimisation criteria may also be included in the problem specification.
Although, several attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision, which is needed both to obtain a formal account of scheduling problem solving and to provide effective support for development of scheduling applications by reuse.
Our library subscribes to the Task-Method-Domain-Application (TMDA) knowledge modelling framework, which provides a structured organisation for the different components of the library. In line with the organisation proposed by TMDA, we first developed a generic scheduling task ontology, which formalises the space of scheduling problems independently of any particular application domain, or problem solving method. Then we constructed a task-specific, but domain independent model of scheduling problem-solving, which generalises from the variety of approaches to scheduling problem-solving, which can be found in literature. The generic nature of this model was demonstrated by constructing seven methods for scheduling, as alternative specialisation of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for the analysis and development of scheduling applications
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Design as interactions of problem framing and problem solving: a formal and empirical basis for problem framing in design
In this thesis, I present, illustrate and empirically validate a novel approach to modelling and explaining design process. The main outcome of this work is the formal definition of the problem framing, and the formulation of a recursive model of framing in design. The model (code-named RFD), represents a formalisation of a grey area in the science of design, and sees the design process as a recursive interaction of problem framing and problem solving.
The proposed approach is based upon a phenomenon introduced in cognitive science and known as (reflective) solution talkback. Previously, there were no formalisations of the knowledge interactions occurring within this complex reasoning operation. The recursive model is thus an attempt to express the existing knowledge in a formal and structured manner. In spite of rather abstract, knowledge level on which the model is defined, it is a firm step in the clarification of design process. The RFD model is applied to the knowledge-level description of the conducted experimental study that is annotated and analysed in the defined terminology. Eventually, several schemas implied by the model are identified, exemplified, and elaborated to reflect the empirical results.
The model features the mutual interaction of predicates ‘specifies’ and ‘satisfies’. The first asserts that a certain set of explicit statements is sufficient for expressing relevant desired states the design is aiming to achieve. The validity of predicate ‘specifies’ might not be provable directly in any problem solving theory. A particular specification can be upheld or rejected only by drawing upon the validity of a complementary predicate ‘satisfies’ and the (un-)acceptability of the considered candidate solution (e.g. technological artefact, product). It is the role of the predicate ‘satisfies’ to find and derive such a candidate solution. The predicates ‘specifies’ and ‘satisfies’ are contextually bound and can be evaluated only within a particular conceptual frame. Thus, a solution to the design problem is sound and admissible with respect to an explicit commitment to a particular specification and design frame. The role of the predicate ‘acceptable’ is to compare the admissible solutions and frames against the ‘real’ design problem. As if it answered the question: “Is this solution really what I wanted/intended?”
Furthermore, I propose a set of principled schemas on the conceptual (knowledge) level with an aim to make the interactive patterns of the design process explicit. These conceptual schemas are elicited from the rigorous experiments that utilised the structured and principled approach to recording the designer’s conceptual reasoning steps and decisions. They include the refinement of an explicit problem specification within a conceptual frame; the refinement of an explicit problem specification using a re-framed reference; and the conceptual re-framing (i.e. the identification and articulation of new conceptual terms)
Since the conceptual schemas reflect the sequence of the ‘typical’ decisions the designer may make during the design process, there is no single, symbol-level method for the implementation of these conceptual patterns. Thus, when one decides to follow the abstract patterns and schemas, this abstract model alone can foster a principled design on the knowledge level. It must be acknowledged that for the purpose of computer-based support, these abstract schemas need to be turned into operational models and consequently suitable methods. However, such operational perspective was beyond the time and resource constraints placed on this research
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