3.2. Behaviours Production Systems
“It is the nature of the mind that makes individuals kin, and the differences in the shape, form,
or manner of the material atoms out of whose intricate relationships that mind is built are
altogether trivial”
—Isaac Asimov
Through this chapter, we will discuss and illustrate the building, in an evolutionary
fashion, of a behaviours production system (BPS), that exhibits many of the principles and
properties present in animal behaviour, following an evolutionary bottom-up approach. We
define a BPS as a system that produces adaptive behaviours to control an autonomous agent.
A BPS must solve the well known action selection problem (ASP), but it needs to be more than
an action selection mechanism (ASM). A BPS is characterized by the following features: (1)
adaptiveness to the environment (preprogrammed, learned, and/or evolved), (2) a set of
autonomous and independent modules interacting among them, (3) behaviours are produced
emergently through the interaction among the different modules that compose the system, also
giving opportunity to other properties to emerge, (4) behaviour patterns emerge from the
execution of simple behaviours through time, (5) new behaviours can be incorporated over the
existing repertoire of behaviours, (6) new principles or properties to improve the behaviour
production can be added taking into account the existing structure and functioning, and (7)
several parameters regulate the behaviour production, and if they are fixed by an observer
through an interface, the results that are originated of this adjustment can be observed (such
as in a virtual laboratory). In this sense, the neuroconnector network of Halperin (Hallam,
Halperin and Hallam, 1994) may be considered as an example of a BPS.
The behaviours production system presented here has been structured from a network
of blackboard nodes (Gonzalez and Negrete, 1997; Negrete and Gonzalez, 1998). We believe
that the blackboard architecture constitutes an ideal scenario for the implementation of
behaviours production systems, due to its capacity of coordination and integration of many
activities in real time. Also, it provides a great flexibility in the incorporation of new
functionality, and it handles the action selection as knowledge selection in the solution of the
problem. Another property of the blackboard architecture is the opportunism in the problem
solving, which is a property of the behaviour production in animals desirable in autonomous
systems.
The evolutionary bottom-up approach followed by us can be described in the following
terms: we will first try to solve one problem, and once we have a BPS that solves this problem,
we will strive to co-evolve the BPS alongside the problem as itself evolves and becomes more
complex, but without losing the capabilities of solving the previous problem(s). In this way, and
taking into account the scheme shown in Figure 1, we will first build a BPS for the problem of
reflex behaviours, which constitutes an initial layer. Then, we will add a second layer to model
reactive behaviours. Next, we will add another layer dealing with the problem of motivated
behaviours, but without losing the functionality of the two previous ones. Finally, we will refine
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