Near knowledge : inductive learning systems in law

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.

Examining the critical interplay of knowledge acquisition and integration capabilities in service innovation-based competitive advantage in project oriented service firms

While past knowledge-based approaches to service innovation have emphasized the role of knowledge integration in the delivery of customer-focused solutions, these approaches do not adequately address the complexities inherent in knowledge acquisition and integration in project-oriented firms. Adopting a dynamic capability framework and building on knowledge-based approaches to innovation, the current study examines how the interplay of learning capabilities and knowledge integration capability impacts service innovation and sustained competitive advantage. This two-stage multi-sample study finds that entrepreneurial project-oriented service firms in their quest for competitive advantage through greater innovation invest in knowledge acquisition and integration capabilities. Implications for theory and practice are discussed and directions for future research provided.

Segregating Discourse Segments from Engineering Documents for Knowledge Acquisition

The broader goal of the research being described here is to automatically acquire diagnostic knowledge from documents in the domain of manual and mechanical assembly of aircraft structures. These documents are treated as a discourse used by experts to communicate with others. It therefore becomes possible to use discourse analysis to enable machine understanding of the text. The research challenge addressed in the paper is to identify documents or sections of documents that are potential sources of knowledge. In a subsequent step, domain knowledge will be extracted from these segments. The segmentation task requires partitioning the document into relevant segments and understanding the context of each segment. In discourse analysis, the division of a discourse into various segments is achieved through certain indicative clauses called cue phrases that indicate changes in the discourse context. However, in formal documents such language may not be used. Hence the use of a domain specific ontology and an assembly process model is proposed to segregate chunks of the text based on a local context. Elements of the ontology/model, and their related terms serve as indicators of current context for a segment and changes in context between segments. Local contexts are aggregated for increasingly larger segments to identify if the document (or portions of it) pertains to the topic of interest, namely, assembly. Knowledge acquired through such processes enables acquisition and reuse of knowledge during any part of the lifecycle of a product.

The Use of Graph Grammars for Model-based Reasoning in Diagnostic Expert Systems

Flasinski M. and Lee M.H., The Use of Graph Grammars for Model-based Reasoning in Diagnostic Expert Systems, Prace Informatyczne, Zeszyty Naukowe Uniwersytetu Jagiellonskiego, 9, 1999, pp147-165.

Construction of Neural Network Classification Expert Systems Using Switching Theory Algorithms

A new family of neural network architectures is presented. This family of architectures solves the problem of constructing and training minimal neural network classification expert systems by using switching theory. The primary insight that leads to the use of switching theory is that the problem of minimizing the number of rules and the number of IF statements (antecedents) per rule in a neural network expert system can be recast into the problem of minimizing the number of digital gates and the number of connections between digital gates in a Very Large Scale Integrated (VLSI) circuit. The rules that the neural network generates to perform a task are readily extractable from the network's weights and topology. Analysis and simulations on the Mushroom database illustrate the system's performance.