Implicit learning of expert chess knowledge

This article discusses how CHREST's mechanisms lead to the implicit learning of a large number of chunks, which underpin (expert) behaviour in a number of domains. Results from chess research are discussed

Augmenting migration statistics with expert knowledge

International migration statistics vary considerably from one country to another in terms of measurement, quality and coverage. Furthermore, immigration tend to be captured more accurately than emigration. In this paper, we first describe the need to augment reported flows of international migration with knowledge gained from experts on the measurement of migration statistics, obtained from a multi-stage Delphi survey. Second, we present our methodology for translating this information into prior distributions for input into the Integrated Modelling of European Migration (IMEM) model, which is designed to estimate migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA), by using recent data collected by Eurostat and other national and international institutions. The IMEM model is capable of providing a synthetic data base with measures of uncertainty for international migration flows and other model parameters.

Onion RBS for Disorders Diagnosis and Treatment

Abstract: This research included the design of an initial expert system which helps farmers and specialists to diagnose and provide appropriate advice on onion plant diseases; furthermore, the management of knowledge used in the expert system was discussed. One of the key elements of this research was to find the appropriate language to diagnose the onion disease and the current situation in the knowledge base. Expert systems to be able to effectively implement the consultation, production rules were used to capture knowledge. The expert system was developed using CLIPS with the Delphi language interface. The expert system has produced good results in the analysis of onion disease cases that have been tested and enable the system to determine the correct diagnosis in all cases

Towards reason: political disputes, public attention and the use of expert knowledge in policymaking

This article examines expert knowledge utilization in public policy processes. We study how much expert knowledge is employed and the extent to which decision-makers deliberate on the information provided by the experts, under various conditions of political disputes and public attention. We suggest four hypotheses. It is proposed that expert knowledge will be used more, but that there will be less deliberation in situations of political disputes. It is also suggested that expert knowledge will be consulted more and the decision-makers will take a more deliberative approach when there is a lot of attention from citizens. Our empirical findings, based on original data from local politics in Sweden, are in line with the hypotheses. The findings highlight the importance of both studying the extent of expert knowledge use and the way expertise is utilized. Another important insight is that politics seem to matter in relation to the role expert knowledge plays in public policymaking.Expert knowledge; Public policy; Political disputes; Public attention; Deliberation; Local government Sweden

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

Extracting causal relationships from Chinese written text

Expert systems form one of the most important research areas in Artificial Intelligence. The main parts in expert systems are knowledge bases and inference engines. In the knowledge bases the main knowledge is knowledge in the form of ``IF-THEN" statements. In knowledge graphs, a new form of knowledge representation, the ``IF-THEN" statements are tied up with causal operators (CAU-relations). In this paper, we picked out some Chinese operators with ``CAU" meaning, and investigated these operators. We also show by an example how to extract causal relations from a given Chinese writing text

Development of a knowledge acquisition tool for an expert system flight status monitor

Two of the main issues in artificial intelligence today are knowledge acquisition dion and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. The knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use is discussed. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base