Learning large-scale spatial relationships in a maze and effects of MK-801 on retrieval in the rhesus monkey

Monkeys have strong abilities to remember the visual properties of potential food sources for survival in the nature. The present study demonstrated the first observations of rhesus monkeys learning to solve complex spatial mazes in which routes were guid

Analogical reasoning in case-based design

A computer can assist the process of design by analogy by recording past designs. The experience these represent could be much wider than that of designers using the system, who therefore need to identify potential cases of interest. If the computer assists with this lookup, the designers can concentrate on the more interesting aspect of extracting and using the ideas which are found. However, as the knowledge base grows it becomes ever harder to find relevant cases using a keyword indexing scheme without knowing precisely what to look for. Therefore a more flexible searching system is needed.

If a similarity measure can be defined for the features of the designs, then it is possible to match and cluster them. Using a simple measure like co-occurrence of features within a particular case would allow this to happen without human intervention, which is tedious and time- consuming. Any knowledge that is acquired about how features are related to each other will be very shallow: it is not intended as a cognitive model for how humans understand, learn, or retrieve information, but more an attempt to make effective, efficient use of the information available. The question remains of whether such shallow knowledge is sufficient for the task.

A system to retrieve information from a large database is described. It uses co-occurrences to relate keywords to each other, and then extends search queries with similar words. This seems to make relevant material more accessible, providing hope that this retrieval technique can be applied to a broader knowledge base.

The retrieval of structured design rationale for the re-use of design knowledge with an integrated representation

Design knowledge can be acquired from various sources and generally requires an integrated representation for its effective and efficient re-use. Though knowledge about products and processes can illustrate the solutions created (know-what) and the courses of actions (know-how) involved in their creation, the reasoning process (know-why) underlying the solutions and actions is still needed for an integrated representation of design knowledge. Design rationale is an effective way of capturing that missing part, since it records the issues addressed, the options considered, and the arguments used when specific design solutions are created and evaluated. Apart from the need for an integrated representation, effective retrieval methods are also of great importance for the re-use of design knowledge, as the knowledge involved in designing complex products can be huge. Developing methods for the retrieval of design rationale is very useful as part of the effective management of design knowledge, for the following reasons. Firstly, design engineers tend to want to consider issues and solutions before looking at solid models or process specifications in detail. Secondly, design rationale is mainly described using text, which often embodies much relevant design knowledge. Last but not least, design rationale is generally captured by identifying elements and their dependencies, i.e. in a structured way which opens the opportunity for going beyond simple keyword-based searching. In this paper, the management of design rationale for the re-use of design knowledge is presented. The retrieval of design rationale records in particular is discussed in detail. As evidenced in the development and evaluation, the methods proposed are useful for the re-use of design knowledge and can be generalised to be used for the retrieval of other kinds of structured design knowledge. © 2012 Elsevier Ltd. All rights reserved.

The retrieval of structured design knowledge

Design rationale is an effective way of capturing knowledge, since it records the issues addressed, the options considered, and the arguments used when specific decisions are made during the design process. Design rationale is generally captured by identifying elements and their dependencies, i.e. in a structured way. Current retrieval methods focus mainly on either the classification of rationale or on keyword-based searches of records. Keyword-based retrieval is reasonably effective as the information in design rationale records is mainly described using text. However, most of the current keyword-based retrieval methods discard the implicit structures of these records, resulting either in poor precision of retrieval or in isolated pieces of information that are difficult to understand. This ongoing research aims to go beyond keyword-based retrieval by developing methods and tools to facilitate the provision of useful design knowledge in new design projects. Our first step is to understand the structured information derived from the relationship between lumps of text held in different nodes in the design rationale captured via a software tool currently used in industry, and study how this information can be utilised to improve retrieval performance. Specifically, methods for utilising various structured information are developed and implemented on a prototype keyword-based retrieval system developed in our earlier work. The implementation and evaluation of these methods shows that the structured information can be utilised in a number of ways, such as filtering the results and providing more complete information. This allows the retrieval system to present results that are easy to understand, and which closely match designers' queries. Like design rationale, other methods for representing design knowledge also in essence involve structured information and thus the methods proposed can be generalised to be adapted and applied for the retrieval of other kinds of design knowledge. Copyright © 2002-2012 The Design Society. All rights reserved.

Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation

The safety of post-earthquake structures is evaluated manually through inspecting the visible damage inflicted on structural elements. This process is time-consuming and costly. In order to automate this type of assessment, several crack detection methods have been created. However, they focus on locating crack points. The next step, retrieving useful properties (e.g. crack width, length, and orientation) from the crack points, has not yet been adequately investigated. This paper presents a novel method of retrieving crack properties. In the method, crack points are first located through state-of-the-art crack detection techniques. Then, the skeleton configurations of the points are identified using image thinning. The configurations are integrated into the distance field of crack points calculated through a distance transform. This way, crack width, length, and orientation can be automatically retrieved. The method was implemented using Microsoft Visual Studio and its effectiveness was tested on real crack images collected from Haiti.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.