17691694
Case-Based Adaptation for UML Diagram Reuse
In this paper we present an approach to software design reuse based on Case-Based Adaptation. We show how this approach is integrated in a CASE tool suggesting solutions to the software designer. This approach generates new UML designs based on previous ones, which are stored in a central repository. Two different strategies are described and evaluated experimentally.
5517190
A Model for Creativity in Creature Generation
This paper brings a proposal and general specification of a new project that aims to apply Conceptual Blending (Fauconnier and Turner 1998) as a process of creature generation in games. The project we describe is based on Divago (Pereira and Cardoso, 2003 and should be able to bring novelty among creatures that populate a game. Moreover, it is expected to extend these capabilities to other objects in a game, towards a very dynamic environment. Since this is a work in progress, we provide the first preliminary results or experimental examples and try to provide the reader with a clear image of the framework to be followed.
11166519
Case Retrieval of Software Designs using WordNet
Software design is one of the most important phases in system development, due to crucial decisions that are made during this phase. The need for software being developed in less time puts a lot of pressure in the design phase. One way to solve this problem is to reuse previous design solutions. In software design reuse the retrieval of relevant designs is a key issue. 
 
Case-Based Reasoning reuses past experiences to solve new problems, providing a reasoning framework for design reuse. But designing software involves reasoning at a more abstract level than coding software, thus a software design reuse tool must be able to work with a broad range of abstract concepts. A possible solution is the use of a common sense ontology, capable of providing this kind of knowledge, otherwise the system would have to demand a lot of knowledge from the designer. 
 
This paper presents an approach to software design retrieval based on Case-Based Reasoning combined with a common sense ontology - WordNet. We describe the case retrieval algorithm, the case similarity metrics and experimental results.
11245975
Experiments with free concept generation in Divago
This paper presents a set of experiments we carried out with, Divago, a system that is an attempt to implement our ideas towards a computational model of creativity. It is expected to be able to generate novel concepts out of previous knowledge. Here we show its behaviour with a large dataset constructed independently by other researchers consisting of over 170 nouns (for a project named C^3). Each noun is represented with a syntax that is equivalent to the one adopted for Divago. We apply a two step experimentation procedure, which starts by ''training'' the system with ''preferred outcomes'' and then allowing it to do free generation, constrained by the pragmatic goal of a given query. We evaluate the results and make a short discussion regarding well-defined criteria of novelty and usefulness. We also present a comparison with a similar experiment done with C^3.
9759378
Noun Sense Disambiguation with WordNet for Software Design Retrieval
Natural language understanding can be used to improve the usability of intelligent Computer Aided Software Engineering (CASE) tools. For a software designer it can be helpful in two ways: a broad range of natural language terms in the naming of software objects, attributes and methods can be used; and the system is able to understand the meaning of these terms so that it could use them in reasoning mechanisms like information retrieval. But, the problem of word sense disambiguation is an obstacle to the development of computational systems that can fully understand natural language. In order to deal with this problem, this paper presents a word sense disambiguation method and how it is integrated with a CASE tool.
15250271
Evaluation of Case-Based Maintenance Strategies in Software Design
CBR applications running in real domains can easily reach thousands of cases, which are stored in the case library. Retrieval times can increase greatly if the retrieval algorithm can not cope with such an amount of cases. Redundancy can also be a problem, focusing retrieval alternatives in a very restricted search space. Basically, the system's performance starts to degrade with the increase of the case-base size. Case-base maintenance allows CBR systems to deal with this problem, mainly through the use of case selection criteria. In this paper we present an experimental study about several case-base maintenance policies developed till now. We adapted and implemented these policies to a CBR system for software reuse and design, testing the applicability of these policies to cases with a complex representation (combination of tree and graph representations).
9947181
The Influence of Learning in the Evolution of Busy Beavers
The goal of this research is to study how individual learning interacts with an evolutionary algorithm in its search for good candidates for the Busy Beaver problem. Two learning models, designed to act as local search procedures, are proposed. Experimental results show that local search methods that are able to perform several modifications in the structure of an individual in each learning step provide an important advantage. Some insight about the role that evolution and learning play during search is also presented.
6376797
Sharing engineering information and knowledge
This paper describes an agent-based recommendation system developed to support knowledge acquisition and sharing processes. Its purpose is to aid the process of community building, specially in the early phase of peer search and recognition in a distributed engineering environment, where multidisciplinary teams must be established. In this kind of problem information about possible candidates to make up the teams must be gathered from all available channels, including the electronic ones. Due to privacy issues, Web based data mining systems are not efficient for the purpose of constructing candidate's profiles, even if they can offer some help; therefore a distributed solution that permits each candidate's workplace to be explored was developed. As a means of aggregating collected knowledge for sharing, a conceptual model, relating candidate's profiles and their knowledge domain was developed in the form of an ontology. This strategy allows the system to be specialised in terms of the candidates' main knowledge domain.
13936575
Shallow Parsing with Conditional Random Fields
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern optimization algorithms were critical in achieving these results. We present extensive comparisons between models and training methods that confirm and strengthen previous results on shallow parsing and training methods for maximum-entropy models.
8920330
On the Evolution of Evolutionary Algorithms
In this paper we discuss the evolution of several components of a traditional Evolutionary Algorithm, such as genotype to phenotype mappings and genetic operators, presenting a formalized description of how this can be attained. We then focus on the evolution of mapping functions, for which we present experimental results achieved with a meta-evolutionary scheme.
2344862
Busy Beaver - The Influence of Representation
The Busy Beaver is an interesting theoretical problem proposed by Rado in 1962, in the context of the existence of non computable functions. In this paper we propose an evolutionary approach to this problem. We will focus on the representational issues, proposing alternative ways of codifying and interpreting Turing Machines, designed to take advantage of the existence of sets of equivalent Turing Machines. The experimental results show that these alternatives provide improvement over the "standard" genetic codification.