Plaza, Arcos and Martin[Plaza, Arcos,
&Mart´n1996] discuss two modes of cooperation among
case-based reasoning (CBR) agents where an agent can leverage the
learning capabilities or past experience of peer agents
to achieve a task or solve a
problem. These modes are developed within the Federated Peer
Learning (FPL) framework [Plaza, Arcos,
&Mart´n1996] that aims to study
cooperative problem solving among agents possessing either same or
different capabilities and incorporating potentially different
knowledge and problem solving behaviors based on their individual
learning and experience. Cooperative problem solving in such a system
can result in bringing wide range of experience to bear on a task at
hand in an agent. The approach taken here to achieve cooperation is
through communication using the Noos representation language
developed at IIIA for integrating learning and problem
solving[Arcos &Plaza1996].
Plural Noos is an extension of Noos
that allows communication and mobile (or ``migrating'') tasks and
methods (to achieve these tasks) among agents that use Noos as
a representation language. In particular, we will show two modes of
cooperation among CBR agents: Distributed Case-based Reasoning
(DistCBR) and Collective
Case-based Reasoning (ColCBR). Intuitively, in DistCBR cooperation
mode an agent 
delegates its authority to another peer
agent 
to solve a
problem - for instance when is
unable to solve it adequately. In contrast, ColCBR cooperation mode
maintains the authority of the originating agent: an agent
can transmit a mobile
method to another agent to be
executed there. That is to say, uses the experience
accumulated by other peer agents while maintaining the control on
how the problem is solved.
Each of the cooperating agents in DistCBR and ColCBR is capable of solving the overall task by itself (most of time) unlike in Negotiated Retrieval where agents are specialists at specific subtasks.