"Today's systems for managing critical infrastructure such as traffic, energy, or industry automation systems are highly complex, distributed, and increasingly decentralized. Multi-agent systems (MAS) provide an intuitive metaphor and configurable, robust and scalable methods for problem-solving and control in distributed, decentrally organized system. The purpose of Distributed Data Mining (DDM) is to provide algorithmic solutions for data analysis in a distributed manner to detect hidden patterns in data and extract knowledge necessary for decentralized decision making. A new promising area of research studies possibilities for coupling MAS and DDM by exploiting DDM methods for improving agents’ intelligence and MAS systems performance.In the ADMIT project we focus on methods for distributed estimation of parameters for the individual agents, agent communities, and application-level information models. Our approach is based on Computational statistics (CST), which includes a set of methods for approximate solution of statistical problems without complex statistical procedures. The goal of the ADMIT project is to develop an agent-oriented DDM framework, which includes a set of computationally effective, robust and easy to apply methods for models parameter estimation and allows easy incorporation into MAS applications to analyze models at different levels of MAS.The scientific research objectives of ADMIT are:1. To develop a conceptual architecture of agent-oriented DDM framework as well as a methodology of its usage in the multiagent programming frameworks;2. To develop a set of computationally effective and reliable to bad data quality CST-based DDM methods, for efficient estimation of the models parameters on the basis of distributed data as well as estimate the methods performance;3. To access the impact of incorporation of the DDM framework to MAS-based applications (with the main focus on traffic and logistics domains)."