Page 166 - DCAP405_SOFTWARE_ENGINEERING

Unit 9: System Engineering

particular we need a positive theory of the environment, that is, some kind of principled Notes
characterization of those structures or dynamics or other attributes of the environment in virtue
of which adaptive behavior is adaptive.
Herbert Simon discussed the issue in his pre-AI work. His book Administrative Behavior, for
example, presents the influential theory that later became known as limited rationality. In
contrast to the assumption of rational choice in classical economics, Simon describes a range of
cognitive limitations that make fully rational decision-making in organizations impracticable.
Yet organizations thrive anyway, he argues, because they provide each individual with a
structured environment that ensures that their decisions are good enough. The division of labor,
for example, compensates for the individual’s limited ability to master a range of tasks. Structured
flows of information, likewise, compensate for the individual’s limited ability to seek this
information out and judge its relevance. Hierarchy compensates for the individual’s limited
capacity to choose goals. And fixed procedures compensate for individuals’ limited capacity to
construct procedures for themselves.
In comparison to Simon’s early theory in Administrative Behavior, AI has downplayed the
distinction between agent and environment. In Newell and Simon’s early work on problem
solving, the environment is reduced to the discrete series of choices that it presents in the course
of solving a given problem. The phrase “task environment” came to refer to the formal structure
of the search space of choices and outcomes. This is clearly a good way of modeling tasks such as
logical theorem-proving and chess, in which the objects being manipulated are purely formal.
For tasks that involve activities in the physical world, however, the picture is more complex. In
such cases, the problem solving model analyzes the world in a distinctive way. Their theory
does not treat the world and the agent as separate constructs. Instead, the world shows up, so to
speak, phenomenological: in terms of the differences that make a difference for this agent, given
its particular representations, actions, and goals. Agents with different perceptual capabilities
and action repertoires, for example, will inhabit different task environments, even though their
physical surroundings and goals might be identical.
Newell and Simon’s theory of the task environment, then, tends to blur the difference between
agent and environment. As a framework for analysis, we find the phenomenological approach
valuable, and we wish to adapt it to our own purposes. Unfortunately, Newell and Simon carry
this blurring into their theory of cognitive architecture. They are often unclear whether problem
solving is an activity that takes place wholly within the mind, or whether it unfolds through the
agent’s potentially complicated interactions with the physical world. This distinction does not
arise in cases such as theorem-proving and chess, or in any other domain whose workings are
easily simulated through mental reasoning. But it is crucial in any domain whose actions have
uncertain outcomes. Even though we wish to retain Newell and Simon’s phenomenological
approach to task analysis, therefore, we do not wish to presuppose that our agents reason by
conducting searches in problem spaces. Instead, we wish to develop an analytical framework
that can guide the design of a wide range of agent architectures. In particular, we want an
analytical framework that will help us design the simplest possible architecture for any given
task.

Continues and Discrete Systems

In models for discrete event dynamic systems (i.e., DEDS models) state changes occur at particular
points in time whose values are not known a priori. As a direct consequence, (simulated) time
advances in discrete ‘jumps’ that have unequal length.
In contrast, with models that emerge from the domain of continuous time dynamic systems (i.e.,
CTDS models), state changes occur continuously (at least in principle) as time advances in a

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