The Development Of Optimization Methods For Knowledge Base Enrichment Processes

The paper presents the concept of approach to the research and evaluation of the processes of intellectual activity associated with the enrichment of the knowledge base. A feature of the research of the process dynamics is the need of simultaneous consideration of such diverse factors as the complexity of information perception, the presence of the deviations of the response from the standard in the process of reproduction and accounting of the test time.A significant influence on the methods of optimization of the knowledge base enrichment process is exerted by a considerable duration of the task learning process. This causes the use of the multifactor experimental design theory to accelerate the progress towards the optimum.The research results can be used in the development of technologies for efficient knowledge assimilation, automation of skills, and also in the development of expert systems for diagnostics of the processes of intellectual activity

Are Routines Reducible or Mere Cognitive Automatisms? Some contributions from cognitive science to help shed light on change in routines

The aim of this article is to understand permanence and changes inside organizational routines. For this purpose, it seems important to explain how individual and collective memorisation occurs, so as to grasp how knowledge can be converted into routines. Although memorisation mechanisms imply a degree of durability, our procedural and declarative knowledge, and our memorisation processes, evolve so that individuals and organisations can project themselves into the future and innovate. Some authors highlight the necessity of dreaming and forgetting (Bergson 1896); others believe that emotions play a role in our memorisation processes (Damasio 1994). These dimensions are not only important at the individual level but also in an organisational context (Lazaric and Denis 2005; Reynaud 2005; Pentland and Feldman 2005).I review the individual dimension of these memorisation processes, with the Anderson’s distinction between procedural knowledge and declarative knowledge. I discuss the notion of cognitive automatisms in order to show why routines should be investigated beyond their first literal assumption (Bargh, 1997). This leads to a clear understanding of the micro level that underpins organisational flexibility and adaptation (notably the motivational triggers). Within organisations, the memorisation mechanisms are at once similar and diverse. Indeed, organisations use their own filters and mechanisms to generate organisational coordination. Organizational memory has its own dimension as it does not merely consist of the sum of individual knowledge and must be able to survive when individuals leave. Routines depend on the organisational memory implemented and on the procedural knowledge and representations of it (individual and collective representations).Knowledge; memorisation; organizations; individuals

A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.Comment: Presented at 3th International Workshop on Multi-Agent Based Simulation, Valencia, Spain, 5th June 201

Delay Learning Architectures for Memory and Classification

We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns by changing the delays of every connection as opposed to modifying the weights. The advantage of this architecture over traditional weight based ones is simpler hardware implementation without multipliers or digital-analog converters (DACs) as well as being suited to time-based computing. The name is derived due to similarity in the learning rule with an earlier architecture called Tempotron. The DELTRON can remember more patterns than other delay-based networks by modifying a few delays to remember the most 'salient' or synchronous part of every spike pattern. We present simulations of memory capacity and classification ability of the DELTRON for different random spatio-temporal spike patterns. The memory capacity for noisy spike patterns and missing spikes are also shown. Finally, we present SPICE simulation results of the core circuits involved in a reconfigurable mixed signal implementation of this architecture.Comment: 27 pages, 20 figure