Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications

Limited Rationality, Formal Organizational Rules, and Organizational Learning (OL)

In spite of a broad agreement among researchers in organizational theory on the importance of rules for the functioning of organizations, most theories of OL neglect or tend to underestimate the role of organizational rules in processes of OL. However, there is one important exception: James G. March, his cooperators and his students. He and Richard Cyert (1963) developed a theory of OL long before this concept became a management fashion. And since that then he and his group have continuously revised and developed this theory. These theories provide fundamental insights into processes of OL, although, so far, they have not yet received adequate recognition in the more popular management literature. These theories assume that complex organizations learn by the ways in which individuals experiment, form inferences and code the lessons of history into rules. OL is based on routines. It is history-dependent and target-oriented. To a large extent OL depends on the relation between observed organizational outcomes and the aspirations set for these outcomes (Levitt and March, 1988: 320). In this article we try to give an introduction into the theories on learning in the March school and link it with our own conceptual and empirical work.

From hard data to soft decision

It is impossible to create model of decision process, as we know nothing about the original decision process. Although it is possible to build models that can get us to the spaces where our fitness is strong enough. These models can contain hard data and soft information as well. In the background of the widely accepted solutions there are transformations of soft information into hard data. This leads us to the world of quantitative decision support. This step is very dangerous! The decision maker uses logic not arithmetic in his thinking process. DoctuS© Knowledge-Based System uses logic. The latest version is also capable of data mining. Using a clusteranalyzing algorithm it can transform the relations between hard data into soft information, which will be used for deduction in reasoning. The number of clusters is given by the user. The cluster-analyzing algorithm makes the clusters using learning example. When running the data mining the clusters remains unchanged and the new data will be transformed. The clusters can be handled using logic. For illustration we use an example of taking decision about location for a power plant

Towards a Characterisation of Assets and Knowledge Created in Technological Agreements Some Evidence from the Automobile-Robotics Sector

This paper tries to bring new insights on the dynamics of inter-firm by focusing on cognitive and organisational dimensions. We consider the knowledge bases created inside the agreement and the characteristics of such knowledge bases (such as tacitness, level of generality, degree of centralisation...). The nature of assets for supporting this creation is also essential for the redeployability of knowledge created. We began by a brief review of some problems encountered by transactions cost economics and present some case studies of agreements between firms in the automobile and robotics sector. After having presented a taxonomy of knowledge and assets involved in such agreements, we bring some new discussion on the exploration/exploitation's dilemma. We argue finally that our taxonomy may be fruitful for a better understanding of the dynamic of firm boudaries by trying to go deeper into the "black box" of agreements.Inter-firm relations, automobile industry, technological agreements

The role of Intangible Assets in the Relationship between HRM and Innovation: A Theoretical and Empirical Exploration

This paper, as far as known, provides a first attempt to explore the role of intellectual capital (IC) and knowledge management (KM) in an integrative way between the relationship of human resource (HR) practices and two types of innovation (radical and incremental). More specifically, the study investigates two sub-components of IC – human capital and organizational social capital. At the same time, four KM channels are discussed, such as knowledge creation, acquisition, transfer and responsiveness.\ud The research is a part of a bigger project financed by the Ministry of Economic Affairs and the province of Overijssel in the Netherlands. The project studies the ‘competencies for innovation’ and is conducted in collaboration with innovative companies in the Eastern part of the Netherlands. \ud An exploratory survey design with qualitative and quantitative data is used for\ud investigating the topic in six companies from industrial and service sector in the region of Twente, the Netherlands. Mostly, the respondents were HR directors. The findings showed that some parts of IC and KM configurations were related to different types of innovation. To make the picture even more complicated, HR practices were sometimes perceived interchangeably with IC and KM by HR directors. Overall, the whole picture about the relationships stays unclear and opens a floor for further research

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed