# [robotics-worldwide] Probabilistic Reasoning and Decision Making in Sensory-Motor Systems - NEW BOOK

Jean-Marc Bollon Jean-Marc.Bollon at inrialpes.fr
Mon Sep 1 07:58:18 PDT 2008

```Probabilistic Reasoning and Decision Making in Sensory-Motor Systems

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Both living organisms and robotic systems must face the same central
difficulty: How to survive being ignorant? How to use an incomplete
and uncertain model of their environment to perceive, infer, decide,
learn and act efficiently?

Indeed, any model of a real phenomenon is incomplete: there are always
some hidden variables, not taken into account in the model, that
influence the phenomenon. The effect of these hidden variables is that
the model and the phenomenon never behave exactly alike. Uncertainty
is the direct and unavoidable consequence of incompleteness. A model
may not foresee exactly the future observations of a phenomenon as
these observations are biased by the hidden variables. It may neither
predict exactly the consequences of its decisions.

Probability theory, considered as an alternative to logic to model
rational reasoning, is the perfect mathematical framework to face this
difficult challenge. Learning is used in a first step to transform
incompleteness into uncertainty, inference is then used to reason and
take decisions based on the probability distributions constructed by
learning. This so-called subjectivist approach to probability allows
uncertain reasoning as complex and formal as the ones made using logic
with exact knowledge.

This book presents twelve different implementations of this approach
to very different sensory-motor systems either by programming robots
or by modeling living systems.

Each of these works summarizes a PhD dissertation defended in
different European universities.

All these works use Bayesian Programming: a mathematical formalism,
which defines in simple mathematical terms the way probability, can be
used as an alternative to logic. Bayesian Programming also proposes a
programming and modeling methodology as, to respect the mathematical
formalism, the programmer should follow always the same steps to build
his model. Finally, Bayesian Programming is a common language to
understand and compare the different models. This language is used all
along this book by all the authors and insures the global coherence of
these twelve very different examples.