15139022
Darwinci: Creating Bridges to Creativity
This paper presents Darwinci, a system that generates new ideas, using a multi-domain knowledge base composed by musical and drawing structures. Its theoretical background comes from the theory of Divergent Production as stated in [7], Genetic Algorithms and metaphor interpretation. Rather than attempting to model human thought, Darwinci’s main goal is to make cross-domain transfer of ideas, in order to create something new, thus becoming a good proposal for creative problem solving.
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.
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.
18713951
Using CBR for Semantic Analysis of Software Specifications
Helping software designers in their task implies the development of tools with intelligent capabilities. One such capability is the integration of natural language understanding in CASE tools, thus improving the designer/tool communication. In this paper, we present a CBR approach for the generation of UML class diagrams from natural language text. This approach is implemented in a CASE tool, with the goal of helping the software designer create the first system model. We describe the natural language conversion module and give an overview of the tool in which it is integrated. Experimental results for retrieval and adaptation mechanisms are also presented.
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).
18592363
Evolving Golomb Rulers
A Golomb Ruler is defined as a ruler that has marks unevenly spaced at integer locations in such a way that the distance between any two marks is unique. Unlike usual rulers, they have the ability to measure more discrete measures than the number of marks they possess. Although the definition of a Golomb Ruler does not place any restriction on the length of the ruler, researchers are usually interested in rulers with minimum length. An Optimal Golomb Ruler (OGR) is defined as the shortest length ruler for a given number of marks [1].
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.
5245012
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finite-state conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.
35936505
Finding Manufacturing Expertise Using Ontologies and Cooperative Agents
The new product development process, in its early phases, congregates knowledge from diverse domains and sources, spread among specialists that usually are not aware of each other’s research interests. This work presents a tool designed to make the establishment of new communities or teams easier. The tool organizes their informal vocabulary into formal domain categories, in order to correlate them, and advises its users of potential networks they could belong to. An approach based on ontologies and machine learning is used to structure and make knowledge acquisition / retrieval, together with agents that cooperate and coordinate the access to the knowledge. The tool automatically updates its knowledge base relieving the user from tasks she/he is unfamiliar with. Initial experiments demonstrated the potential of this kind of tool. A test case is being developed encompassing members of a scientific network in the manufacturing area. Results will be analyzed envisaging its broader use and application to different knowledge domains.
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.