Obtaining an animal welfare status in Norwegian dairy herds—A mountain to climb

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Distributed Representations for Analogical Mapping

Mapping is an important process of analogical reasoning, where correspondences between elements of a previous solution and a new problem are established. After recalling potential analogs from a long-term memory based on surface similarities, the following mapping process captures deeper similarities by establishing correspondences between elements based on common relational structures. An analogical mapping process is also constrained by a semantic component, which increases the probability for establishing correspondences between elements in the two analogs with similar meaning. A technique for applying neural networks in the analogical mapping process that includes both structural and semantic constraints is described in this paper. An experiment from object-oriented software engineering is presented, and reports promising results

Connectionist-based Analogical Mapping of Object-Oriented Specifications: A Representational Scheme

In recent years the focus on software component reuse has been elevated to an important research topic within software engineering circles. This is particularly visible among object-oriented communities. Although reuse is not new to object-oriented software developers, it has matured sufficiently to be applied in the early phases of the development life-cycle. In the ROSA (Reuse of Object-oriented Specifications through Analogy) project (Tessem et al. 1994) the aim is to apply analogical reasoning in the reuse of object-oriented specifications in the initial phase of the software development. This paper address distributed connectionist networks in the analogical mapping process of object-oriented specifications. OOram (Object-Oriented Role Analysis and Modeling) role models (Reenskaug et al. 1996) have selected for component specifications. The role models are represented as directed graphs in the mapping process. This paper shows that analogical mapping of OOram role models is not a ..

An Object-Oriented Approach to Neural Networks

The aim of this report is to present an object-oriented approach to the design of a neural network simulation system. Although there are several object-oriented neural network systems available, only a few presents their design by using a consistent and uniform object-oriented methodology. Object-oriented programming alone is not sufficient to obtain the advantages of object-orientation, such as improving reuse, and emphasizing extensibility and flexibility. A neural network simulation software, called MANN (Modular Artificial Neural Network), is developed by using the OMT (Object Modeling Technique) methodology [6]. An important experience gained from the development process is that the concepts of reuse, extensibility, and flexibility must be included in an early stage of the development to be fully exploited. Keywords: Neural networks, object-oriented design, C++ 1 Introduction For some years, considerable effort has been done in the development of software simulation systems fo..

Distributed Representations of Object-Oriented Specifications for Analogical Mapping

In this paper a distributed connectionist-based algorithm for analogical mapping is introduced, where the Labeling Recursive Auto-Associative Memory (LRAAM) (Sperduti 1993) network is used for computing "distributed representations" of object-oriented specification components. Two types of constraints are considered in this work: semantic and structural. Semantic constraints impose the restriction that the elements, one from a previous solved solution and another from a new problem, must share some common meaning to be mapped, whereas structural constraints impose the restriction that the elements must exist in similar structural relationships to be mapped. The proposed mapping algorithm is tested in the domain of software engineering, where the aim is to use analogical reasoning in the reuse process of object-oriented specifications. This paper demonstrates that under certain conditions the LRAAM network is capable of computing similar "distributed representations" for similar object-..

A Hybrid Model for Combining Structural and Semantic Constraints on Analogical Mapping

Mapping is an important process of analogical reasoning, by establishing correspondences between elements of previous solutions and new problem. After recalling potential analogs from a long-term memory based on surface similarities, a subsequent mapping process captures deeper similarities by establishing correspondences between elements based on common relational structures (Falkenhainer et al. 1989). An analogical mapping process is also constrained semantically, where the probability of mapping elements in the two analogs increases if they have similar meaning. In this paper we describe a technique for applying neural networks in the analogical mapping process that includes both structural and semantic constraints. Directed graphs related to reuse of object-oriented specifications are used in a set of experiments and report promising results

The Epistemology of Learning in Artificial Neural Networks

The aim of this paper is to validate the claim that neural networks appear to have much in common with the behavioristic view of learning. Neural networks are, according to Rumelhart & McClelland (1986), not behavioristic because of their explicit concern with the problem of internal representation and "mental" processing. The claim that neural networks are behavioristic has epistemological implications. Neural network learning theories, hereunder supervised and unsupervised learning, are compared to psychological learning theories in the two epistemological doctrines: empiricism and rationalism. The results indicate that neural networks exhibit interesting features of self-organization, implicit clustering of inner representation, and plasticity. However, the discussion also indicates that neural networks have similar features as the behavioristic learning theories of psychology. Keywords: Philosophy of Science, Learning theories, Neural Networks, Artificial Intelligence I want to ..