Learning
One of the fundamental capabilities of intelligent agents is
the ability to modify its behavior based on experience, to learn.
It is generally accepted that any architecture which attempts
to behave intelligently must have some learning component.
However, the nature of learning supported by the architecture
varies widely, from minimal support to a primary mechanism.
Learning and planning generally work
together to a large extent, with planning generating opportunities
for learning and learning helping to generate more efficient plans.
An important condition for planning from experience is the ability
to represent the world in symbolic
form, in order to identify important features of the evironment
and reason about them.
There are many different types of learning which have been observed
in human behavior, and each architecture listed below attempts to
emulate one or more of them. The following list provides a
general description of the major identifiable learning types.
- Explanation-Based Learning
- EBL is a technique for capturing the results of processing performed
by the agent into a compact representation for efficient retrieval the
next time the agent encounters a similar problem. Most systems implement
EBL by backtracing through problem-solving, summarizing the trace,
and generalizing the result so that it is applicable to other problems.
- Case-Based Learning
- This technique maintains a list of cases in which a particular event
occurred, and tries to detect commonalities in the cases which led to
the event. The commonalities can now be considered preconditions for the
event, and planning can take advantage of this knowledge when its goal
is to achieve the event.
- Learning by Abstraction
- Abstraction learning attempts to express states and operators at a
higher level to simplify processing.
- Learning by Instruction
- Tutorial learning involves taking instructions from another agent
and learning from them in order to accomplish similar tasks alone.
- Learning by Analogy
- This technique transforms solutions to problems in one domain to
solutions to problems in another domain by discovering analogous states
and operators in the two domains.
- Experimentation
- When knowledge is incomplete, experimentation takes place to
discover new knowledge about a particular domain
The following list of architectures include learning components.
Back to List of Other Capablities