|
|
 |
|
Viewing report
|
|
|
|
Epistemic Logic Planning. Edition No. 1
VDM Publishing House, Sep 2008, Pages: 120
Artificial Intelligence algorithms can be divided
into two groups according to the type of problems
they solve. Knowledge-intensive domains contain
explicit knowledge, whereas knowledge-poor domains
contain implicit knowledge. Rules, decision trees and
logical methods are more suitable for the first type.
Neural networks and case-based reasoning (CBR) are
more suitable for the second type.
This project combines the inferencing power of
epistemic logic (type 1) in the adaptation of CBR
with the performance of case-based planning (type 2).
This method is proved to be more efficient then using
planning algorithms alone.
Here a simulated robot is assigned to deliver
parts in a factory. The robot needs to plan the path
that it should choose to achieve its goals and uses
epistemic logic to solve problems. Planning
algorithms are computationally expensive. CBR, using
KNN is used to make the process faster. A STRIPS
planner creates plans. The manager defines the
problem, KNN extracts a plan and a logic sub-system
adapts it according to belief revision theorems. The
new plan is retained for future use.
|
|
|
|
