  We present IBSEAD or distributed autonomous entity systems based Interaction
- a learning algorithm for the computer to self-evolve in a self-obsessed
manner. This learning algorithm will present the computer to look at the
internal and external environment in series of independent entities, which will
interact with each other, with and/or without knowledge of the computer's
brain. When a learning algorithm interacts, it does so by detecting and
understanding the entities in the human algorithm. However, the problem with
this approach is that the algorithm does not consider the interaction of the
third party or unknown entities, which may be interacting with each other.
These unknown entities in their interaction with the non-computer entities make
an effect in the environment that influences the information and the behaviour
of the computer brain. Such details and the ability to process the dynamic and
unsettling nature of these interactions are absent in the current learning
algorithm such as the decision tree learning algorithm. IBSEAD is able to
evaluate and consider such algorithms and thus give us a better accuracy in
simulation of the highly evolved nature of the human brain. Processes such as
dreams, imagination and novelty, that exist in humans are not fully simulated
by the existing learning algorithms. Also, Hidden Markov models (HMM) are
useful in finding "hidden" entities, which may be known or unknown. However,
this model fails to consider the case of unknown entities which maybe unclear
or unknown. IBSEAD is better because it considers three types of entities-
known, unknown and invisible. We present our case with a comparison of existing
algorithms in known environments and cases and present the results of the
experiments using dry run of the simulated runs of the existing machine
learning algorithms versus IBSEAD.
