[16 May 2017] A bachelor student at RTSLAB was awarded the best thesis
award from IDA - Tim Hultman. more ...
[12 May 2016] A master student at RTSLAB was awarded the best thesis
award from IDA - Alexander Alesand. more ...
[12 May 2016] A bachelor student at RTSLAB was awarded the best thesis
award from IDA - Mathias Almquist and Viktor Almquist. more ...
[25 May 2015] A master student at RTSLAB was awarded the best thesis
award from IDA - Klervie Toczé. more ...
[26 May 2014] A bachelor student at RTSLAB was awarded the best thesis
award from IDA - Simon Andersson. more ...
[31 May 2012] A masters student at RTSLAB was awarded the best thesis
award from IDA - Ulf Magnusson. more ...
[27 February 2008] A masters student at RTSLAB was awarded the best thesis
award from IDA - Johan Sigholm. more ...
[03 March 2004] A masters student at RTSLAB was awarded the best thesis
award from IDA - Tobias Chyssler. more ...
[01 Jul 2003] For second year in a row a masters student at RTSLAB was awarded the best thesis
award from SNART - Mehdi Amirijoo. more ...
Master Thesis - Past Projects - Abstract
Rule System with Adaptive Trigger Conditions
Controlling autonomous aircraft is an area within
artificial intelligence that is studied at Saab AB
in Linköping. The behaviour of a simulated
autonomous aircraft can be decided by a system of
rules, which is normally formulated as a set of
conditional statements that respond to certain
values in sensor signals from the environment. This
thesis designs and evaluates a method for
introducing learning abilities to the rule system
that exercises control over a number of aircraft in
a flight simulation.
The rules controlling the aircraft are composed of
three parts -N a triggering condition, an action and
a consequence condition. When the triggering
condition becomes true the action may be executed.
The consequence condition specifies what an
execution of the rule must fulfill to be considered
The learning approach described here assumes a
system that receives an initial set of rules from a
programmer, and improves its behaviour by tuning the
trigger conditions of those rules. Every decision
that the system makes is evaluated by comparing the
actual result from executing the rule with the
intended result, which is stated by the consequence
condition. The resulting rate of success is stored,
so that the accuracy of the system's response to
succeeding situations can be improved by using the
statistical data resulting from earlier decisions.
A set of test cases, focusing on formation flight,
evaluates the learning abilities of the system. For
each of these cases, the system is given an initial
set of rules in which many rules are often
applicable to the same situation. Rule systems, like
expert systems, practically let their rules execute
in parallel, unless conflicts arise. By trying each
of those rules that are competing for being used in
a specific situation, this system can learn which
rule gives the best performance for the current
conditions. Exploiting this feature makes the task
of constructing rules less time consuming, since
many alternative rules can be initially applied to
situations where it is not obvious to the
rule-programmer which one will perform the best.
The thesis concludes that the implemented rule
system meets the basic requirements for adaptivity
and expressiveness in the area of basic formation
flight. Solutions generated by the current system
are not optimal, but the report suggests some
methods for significantly improving its performance.
Author(s): Knut Nordin
Contact: Nancy Reed
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