UNIVERSITY OF HERTFORDSHIRE
		 COMPUTER SCIENCE RESEARCH COLLOQUIUM

			      presents

             "Be Empowered: Guiding Adaptation through 
                   Potential Information Flows"

                        Dr. Daniel Polani 
                   (School of Computer Science, 
                   University of Hertfordshire) 

                    6 February 2008 (Wednesday)

                        Lecture Theatre E350
		     Hatfield, College Lane Campus
		             3 - 4 pm

	       Coffee/tea and biscuits will be available.

		    Everyone is Welcome to Attend


Abstract:

The central importance of (Shannon) information as a resource for the
adaptation of living organisms or agents has been increasingly
established in the last years. The information perspective for the
characterization of an agent's operation is highly attractive since it
provides a universal currency and language for any kind of
"information processing" taking place both within the agent and in the
dynamics of its interaction with the environment, and allows one to
adopt an extreme bottom-up view. Furthermore, it is "coordinate-free"
in the sense that it allows one to formulate principles and "balance
sheets" without having to refer to a particular information processing
mechanism

The use of Causal Bayesian Networks (CBNs) has been established as a
successful technique to create informational models of agents and
their perception-action loop. This technique allows the tracking of
information flows through the composite agent-environment system, the
generalization of Ashby's Law of Requisite Variety, or the application
of generalized Infomax principles. In particular, the latter provide a
path for the generation of structured information processing
architectures with no assumptions beyond the agent being "embodied" in
some structured environment. Phenomena such as active sensing emerge
from the principle as a natural side effect.

The transparency of modelling the perception-action loop using the CBN
formalism allows one to identify additional phenomena and quantities
of interest. Specifically, in the present talk, I will introduce and
discuss "empowerment" which is essentially the amount of potential
information that an agent could inject into the environment via its
actuators and recapture via its sensors. In the simplest of cases,
this reduces to an agent-external channel capacity, but in general one
requires CBNs to formulate empowerment.

In a situation where an agent has no prior preferences, its
empowerment turns out to provide a utility which draws it to
"interesting" states in the system. Since empowerment only depends on
the embodiment of the agent, it can assign sensible preferences to
states even in absence of any other prespecified drives (quantities of
this kind we term "universal utility"). Understanding properties of
possible universal utilities is particularly relevant for the success
of adaptive systems, as the latter frequently have to be able to cope
with novel situations that have not been previously encountered and
for which the systems' innate drives are not appropriate or suitable
drives may not exist yet at all.

I will show how, in a number of scenarios of varied quality and
characteristics, the behaviour resulting from empowerment optimization
is strikingly close to our intuitive expectations, sometimes achieved
in a surprisingly nontrivial way. In the discussion, I will suggest
possible reasons for this and discuss lines for future research.

(This joint work with Alexander S. Klyubin and Chrystopher L. Nehaniv).

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Hertfordshire Computer Science Research Colloquium
http://homepages.feis.herts.ac.uk/~nehaniv/colloq