How much of commonsense and legal reasoning is formalizable? A review of conceptual obstacles

Fifty years of effort in artificial intelligence (AI) and the formalization of legal reasoning have produced both successes and failures. Considerable success in organizing and displaying evidence and its interrelationships has been accompanied by failure to achieve the original ambition of AI as applied to law: fully automated legal decision-making. The obstacles to formalizing legal reasoning have proved to be the same ones that make the formalization of commonsense reasoning so difficult, and are most evident where legal reasoning has to meld with the vast web of ordinary human knowledge of the world. Underlying many of the problems is the mismatch between the discreteness of symbol manipulation and the continuous nature of imprecise natural language, of degrees of similarity and analogy, and of probabilities

Analyze of the Measuring Performance for Artificially Business Intelligent Systems

This paper analyzes the measuring performance of artificially business intelligent systems. Thousands of persons-years have been devoted to the research and development in the vari¬ous aspects of artificially intelligent systems. Much progress has been attained. However, there has been no means of evaluating the progress of the field. How can we assess the cur¬rent state of the science? Most of business intelligent systems are beginning to be deployed commercially. How can a commercial buyer evaluate the advantages and disadvantages of the intelligent candidate and decide which system will perform best for their business applica¬tion? If constructing a system from existing components, how does one select the one that is most appropriate within the desired business intelligent systems? The ability to measure the capabilities of business intelligent systems or components is more that an exercise in satisfy¬ing intellectual or philosophical curiosity. Without measurements and subsequent quantitative evaluation, it is difficult to gauge progress. It is both in a spirit of scientific enquiry and for pragmatic motivations that we embark on the quest for metrics for performance and intelli¬gence of business intelligent systems.artificially intelligent systems, analyze of the measuring performance, business intelligent systems, metrics for performance, meas¬urement performance