10 research outputs found
Multi-perspective modelling for knowledge management and knowledge engineering
ii It seems almost self-evident that âknowledge management â and âknowledge engineeringâ should be related disciplines that may share techniques and methods between them. However, attempts by knowledge engineers to apply their techniques to knowledge management have been praised by some and derided by others, who claim that knowledge engineers have a fundamentally wrong concept of what âknowledge managementâ is. The critics also point to specific weaknesses of knowledge engineering, notably the lack of a broad context for the knowledge. Knowledge engineering has suffered some criticism from within its own ranks, too, particularly of the ârapid prototyping â approach, in which acquired knowledge was encoded directly into an iteratively developed computer system. This approach was indeed rapid, but when used to deliver a final system, it became nearly impossible to verify and validate the system or to maintain it. A solution to this has come in the form of knowledge engineering methodology, and particularly in the CommonKAD
at the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011)
Technical Report TR-2011/1, Department of Languages and Computation. University of Almeria November 2011. JoaquĂn Cañadas, Grzegorz J. Nalepa, Joachim Baumeister (Editors)The seventh workshop on Knowledge Engineering and Software Engineering (KESE7) was held at the Conference of the Spanish Association for Artificial Intelligence (CAEPIA-2011) in La Laguna (Tenerife), Spain, and brought together researchers and practitioners from both fields of software engineering and artificial intelligence. The intention was to give ample space for exchanging latest research results as well as knowledge about practical experience.University of AlmerĂa, AlmerĂa, Spain. AGH University of Science and Technology, KrakĂłw, Poland. University of WĂŒrzburg, WĂŒrzburg, Germany
Knowledge Extraction and Summarization for Textual Case-Based Reasoning: A Probabilistic Task Content Modeling Approach
Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) technique that
has been successfully used for building knowledge systems for tasks/domains where different knowledge sources are easily available, particularly in the form of problem solving situations, known as cases. Cases generally display a clear
distinction between different components of problem solving, for instance, components of the problem description and of the problem solution. Thus, an existing and explicit structure of cases is presumed. However, when problem solving experiences are stored in the form of textual narratives (in natural language), there is no explicit case structure, so that CBR cannot be applied directly.
This thesis presents a novel approach for authoring cases from episodic textual
narratives and organizing these cases in a case base structure that permits a
better support for user goals. The approach is based on the following fundamental ideas:
- CBR as a problem solving technique is goal-oriented and goals are realized by
means of task strategies.
- Tasks have an internal structure that can be represented in terms of
participating events and event components.
- Episodic textual narratives are not random containers of domain concept
terms. Rather, the text can be considered as generated by the underlying
task structure whose content they describe.
The presented case base authoring process combines task knowledge with Natural
Language Processing (NLP) techniques to perform the needed knowledge extraction
and summarization
Knowledge Acquisition Analytical Games: games for cognitive systems design
Knowledge discovery from data and knowledge acquisition from experts are steps of paramount importance when designing cognitive systems. The literature discusses extensively on the issues related to current knowledge acquisition techniques. In this doctoral work we explore the use of gaming approaches as a knowledge acquisition tools, capitalising on aspects such as engagement, ease of use and ability to access tacit knowledge. More specifically, we explore the use of analytical games for this purpose. Analytical game for decision making is not a new class of games, but rather a set of platform independent simulation games, designed not for entertainment, whose main purpose is research on decision-making, either in its complete dynamic cycle or a portion of it (i.e. Situational Awareness). Moreover, the work focuses on the use of analytical games as knowledge acquisition tools. To this end, the Knowledge Acquisition Analytical Game (K2AG) method is introduced. K2AG is an innovative game framework for supporting the knowledge acquisition task. The framework introduced in this doctoral work was born as a generalisation of the Reliability Game, which on turn was inspired by the Risk Game. More specifically, K2AGs aim at collecting information and knowledge to be used in the design of cognitive systems and their algorithms. The two main aspects that characterise those games are the use of knowledge cards to render information and meta-information to the players and the use of an innovative data gathering method that takes advantage of geometrical features of simple shapes (e.g. a triangle) to easily collect players\u2019 beliefs. These beliefs can be mapped to subjective probabilities or masses (in evidence theory framework) and used for algorithm design purposes. However, K2AGs might use also different means of conveying information to the players and to collect data. Part of the work has been devoted to a detailed articulation of the design cycle of K2AGs. More specifically, van der Zee\u2019s simulation gaming design framework has been extended in order to account for the fact that the design cycle steps should be modified to include the different kinds of models that characterise the design of simulation games and simulations in general, namely a conceptual model (platform independent), a design model (platform independent) and one or more implementation models (platform dependent). In addition, the processes that lead from one model to the other have been mapped to design phases of analytical wargaming. Aspects of game validation and player experience evaluation have been addressed in this work. Therefore, based on the literature a set of validation criteria for K2AG has been proposed and a player experience questionnaire for K2AGs has been developed. This questionnaire extends work proposed in the literature, but a validation has not been possible at the time of writing. Finally, two instantiations of the K2AG framework, namely the Reliability Game and the MARISA Game, have been designed and analysed in details to validate the approach and show its potentialities
Object-oriented knowledge acquisition: Integrating construction of and reasoning in object-oriented knowledge bases
PĂ€ivikki Parpola presents in this research report the SeSKA (seamless structured knowledge acquisition) methodology, integrating phases of knowledge acquisition (KA) through seamless transformations between object-oriented (OO) models. This attacks the problem of disintegration, or the gap between phases. The methodology is accompanied by presentation of the SOOKAT (structured object-oriented knowledge acquisition) tool supporting it. SeSKA and SOOKAT extend the KA process to constructing knowledge bases by instantiating a series of models for inferencing. The models are constructed in SOOKAT utilizing metaobject protocols.
Inferences performed in instantiations of OO models are guided by control objects (CO). Messages are sent between COs and components of the inference structure. A specific CO, possibly using subordinate COs, can be specified for each inference strategy.
There exists a mutual CO for forward and backward chaining that can also be used when reasoning according to protocols. In addition, COs for problem-solving methods (PSMs), such as cover-and-differentiate or propose-and-revise, can be used.Three example applications are used for demonstrating the properties of the SeSKA methodology and SOOKAT, that is, a mineral classification "toy application", Sisyphus III rock classification and dietary management of multiple sclerosis.Mechanisms for importing problem-solving methods (PSMs) over the Internet, as well as for generating specific control objects (COs) for them, remain open to further development.Â
PĂ€ivikki Parpola (1965-2015) was a Ph.D. student at Aalto University. Her research interests concerned knowledge acquisition and presentation, development and reasoning in expert systems for different application fields, using the object-oriented paradigm. She received her M.Sc. in 1988 and Lic.Phil. in 1995 from the Department of Computer Science at the University of Helsinki. Her M.Sc. thesis concerned forming a formal grammar based on text samples of natural language or unknown writing. Research presented in her Lic.Phil. thesis continued in her Ph.D. studies. She worked with Nokia Research Center from 1987 to 1993. In addition to her thesis, she published multiple international and domestic conference papers and articles as well as contributed in European Union research project publications
Knowledge based requirements specification for reconfigurable assembly systems
Automated assembly technology may be the key to sustaining manufacturing industry in more developed countries. Currently this comprises dedicated systems that can assemble single products at high volumes and flexible systems to assemble a wide variety of products in low volumes. However, competitive forces demand a compromise between the two and Reconfigurable Assembly Systems are an avenue for achieving high volume and high variety production.
Although this technology is coming to the fore, there is a distinct lack of tools and methods that make the prospect attractive to key decision makers in organisations. Reconfigurable solutions, which may be profitable in the long term, are rejected in favour of short term solutions, which prove to be more expensive over time.
The benefits of requirements engineering have been exploited in software engineering and this work demonstrates how these can be adapted to an assembly environment to form a new basis for communication between the system vendors, who supply assembly system solutions, and system users, who use them.
Knowledge Engineering has become a key aspect in industry due to the challenges of retaining personnel and their knowledge within organisations. This is because employees take their knowledge of the organisation with them when they leave. The retention of this knowledge would help to maintain the continuity within organisations.
This thesis reports on research that aims to provide a means to integrate these three aspects to form a basis for sustaining competitive manufacture in more developed countries.
Moreover, Knowledge Based Requirements Specification for Reconfigurable Assembly Systems will provide a vital medium for promoting Reconfigurable Assembly Systems and encourage their implementation by providing a knowledge-based platform for the specification of Reconfigurable Assembly Systems
A Prototype Method and Tool to Facilitate Knowledge Sharing in the New Product Development Process
New Product Development (NPD) plays a critical role in the success of manufacturing
firms. Activities in the product development process are dependent on the exchange of
knowledge among NPD project team members. Increasingly, many organisations
consider effective knowledge sharing to be a source of competitive advantage.
However, the sharing of knowledge is often inhibited in various ways.
This doctoral research presents an exploratory case study conducted at a
multinational physical goods manufacturer. This investigation uncovered three,
empirically derived and theoretically informed, barriers to knowledge sharing. They
have been articulated as the lack of an explicit definition of information about the
knowledge used and generated in the product development process, and the absence of
mechanisms to make this information accessible in a multilingual environment and to
disseminate it to NPD project team members. Collectively, these barriers inhibit a
shared understanding of product development process knowledge. Existing knowledge
management methodologies have focused on the capture of knowledge, rather than
providing information about the knowledge and have not explicitly addressed issues
regarding knowledge sharing in a multilingual environment.
This thesis reports a prototype method and tool to facilitate knowledge sharing
that addresses all three knowledge sharing barriers. Initially the research set out to
identify and classify new product development process knowledge and then sought to
determine what information about specific knowledge items is required by project
teams. Based on the exploratory case findings, an ontology has been developed that
formally defines information about this knowledge and allows it to be captured in a
knowledge acquisition tool, thereby creating a knowledge base. A mechanism is
provided to permit language labels to be attached to concepts and relations in the
ontology, making it accessible to speakers of different languages. A dissemination tool
allows the ontology and knowledge base to be viewed via a Web browser client.
Essentially, the ontology and mechanisms facilitate a knowledge sharing capability.
Some initial validation was conducted to better understand implementation issues and
future deployment of the prototype method and tool in practice
No Optimisation Without Representation: A Knowledge Based Systems View of Evolutionary/Neighbourhood Search Optimisation
Centre for Intelligent Systems and their ApplicationsIn recent years, research into âneighbourhood searchâ optimisation techniques such as simulated
annealing, tabu search, and evolutionary algorithms has increased apace, resulting in a
number of useful heuristic solution procedures for real-world and research combinatorial and
function optimisation problems. Unfortunately, their selection and design remains a somewhat
ad hoc procedure and very much an art. Needless to say, this shortcoming presents real
difficulties for the future development and deployment of these methods.
This thesis presents work aimed at resolving this issue of principled optimiser design. Driven
by the needs of both the end-user and designer, and their knowledge of the problem domain
and the search dynamics of these techniques, a semi-formal, structured, design methodology
that makes full use of the available knowledge will be proposed, justified, and evaluated. This
methodology is centred around a Knowledge Based System (KBS) view of neighbourhood
search with a number of well-defined knowledge sources that relate to specific hypotheses
about the problem domain. This viewpoint is complemented by a number of design heuristics
that suggest a structured series of hillclimbing experiments which allow these results to be
empirically evaluated and then transferred to other optimisation techniques if desired.
First of all, this thesis reviews the techniques under consideration. The case for the exploitation
of problem-specific knowledge in optimiser design is then made. Optimiser knowledge is
shown to be derived from either the problem domain theory, or the optimiser search dynamics
theory. From this, it will be argued that the design process should be primarily driven by
the problem domain theory knowledge as this makes best use of the available knowledge and
results in a system whose behaviour is more likely to be justifiable to the end-user.
The encoding and neighbourhood operators are shown to embody the main source of problem
domain knowledge, and it will be shown how forma analysis can be used to formalise the
hypotheses about the problem domain that they represent. Therefore it should be possible
for the designer to experimentally evaluate hypotheses about the problem domain. To this
end, proposed design heuristics that allow the transfer of results across optimisers based on a
common hillclimbing class, and that can be used to inform the choice of evolutionary algorithm
recombination operators, will be justified. In fact, the above approach bears some similarity to
that of KBS design. Additional knowledge sources and roles will therefore be described and
discussed, and it will be shown how forma analysis again plays a key part in their formalisation.
Design heuristics for many of these knowledge sources will then be proposed and justified.
This methodology will be evaluated by testing the validity of the proposed design heuristics in
the context of two sequencing case studies. The first case study is a well-studied problem from
operational research, the flowshop sequencing problem, which will provide a through test of
many of the design heuristics proposed here. Also, an idle-time move preference heuristic will
be proposed and demonstrated on both directed mutation and candidate list methods.
The second case study applies the above methodology to design a prototype system for resource
redistribution in the developing world, a problem that can be modelled as a very large
transportation problem with non-linear constraints and objective function. The system, combining
neighbourhood search with a constructive algorithm which reformulates the problem
to one of sequencing, was able to produce feasible shipment plans for problems derived from
data from the World Health Organisationâs TB programme in China that are much larger than
those problems tackled by the current âstate-of-the-artâ for transportation problems