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The ANALOG Project

The aim of the Analog project is to develop framework for integrating problem solving methods and learning methods. The approach followed is developing Noos, a reflective object-centered representation language that supports modelling and implementing both problem solving methods and learning methods in an integrated, seamless way. This research is a follow-up of the Massive Memory Project.

Currently, learning methods integrated in Noos include:

Currently, implemented problem solving methods include

There is a Progress Report on the current status of the Analog project, including relevant articles and reports.


Case-based Reasoning with Symbolic Similitudes

On the Importance of Similitude: An Entropy-based Assessment
Enric Plaza, Ramon López de Mántaras, and Eva Armengol
[Compressed PS | Compressed PDF]

Inference, Learning and Reflection in Noos

Inference and reflection in the object-centered representation language Noos
Noos: an integrated framework for problem solving and learning

Federated Learning in Multiagent Systems

Cooperation among Case-based Reasoning Agents

Formalizing Case-based Reasoning

A Logical Approach to Case-Based Reasoning Using Fuzzy Similarity Relations

Other Publications and Reports

See the Analog Project Progress Report and more generally see the IIIA publications and reports.

For more information

[Plural] [Cooperative CBR] [Federated Learning] [Features Terms] [Noos] [Team Members]