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<p>Artificial intelligence is a broad research area and there are many different reasons why it is interesting to study artificial intelligence. One of the main reasons is to understand how information might be represented in the human brain. The Recursive Auto Associative Memory (RAAM) is a connectionist architecture that with some success has been used for that purpose since it develops compact distributed representations for compositional structures.</p><p>A lot of extensions to the RAAM architecture have been developed through the years in order to improve the performance of RAAM; Bi coded RAAM (B-RAAM) is one of those extensions. In this work a modified B-RAAM architecture is tested and compared to RAAM regarding: Training speed, ability to learn with smaller internal representations and generalization ability. The internal representations of the two network models are also analyzed and compared. This dissertation also includes a discussion of some theoretical aspects of B-RAAM.</p><p>It is found here that the training speed for B-RAAM is considerably lower than RAAM, on the other hand, RAAM learns better with smaller internal representations and is better at generalize than B-RAAM. It is also shown that the extracted internal representation of RAAM reveals more structural information than it does for B-RAAM. This has been shown by hieratically cluster the internal representation and analyse the tree structure. In addition to this a discussion is added about the justifiability to label B-RAAM as an extension to RAAM.</p>

Artificial intelligence is a broad research area and there are many different reasons why it is interesting to study artificial intelligence. One of the main reasons is to understand how information might be represented in the human brain. The Recursive Auto Associative Memory (RAAM) is a connectionist architecture that with some success has been used for that purpose since it develops compact distributed representations for compositional structures. A lot of extensions to the RAAM architecture have been developed through the years in order to improve the performance of RAAM; Bi coded RAAM (B-RAAM) is one of those extensions. In this work a modified B-RAAM architecture is tested and compared to RAAM regarding: Training speed, ability to learn with smaller internal representations and generalization ability. The internal representations of the two network models are also analyzed and compared. This dissertation also includes a discussion of some theoretical aspects of B-RAAM. It is found here that the training speed for B-RAAM is considerably lower than RAAM, on the other hand, RAAM learns better with smaller internal representations and is better at generalize than B-RAAM. It is also shown that the extracted internal representation of RAAM reveals more structural information than it does for B-RAAM. This has been shown by hieratically cluster the internal representation and analyse the tree structure. In addition to this a discussion is added about the justifiability to label B-RAAM as an extension to RAAM.

Rekursive Auto-Assoziative Speicher (RAAM) sind spezielle Neuronale Netze (NN), die in der Lage sind, hierarchiche Strukturen zu verarbeiten. Bei der Simulation dieser Netze gibt es einige Besonderheiten, wie z.B. die dynamische Trainingsmenge, zu beachten. In der Arbeit werden diese und die daraus resultierenden angepassten Lernalgorithmen erörtert. Außerdem wird ein normaler Backpropagation-Simulator (Xerion) um die Fähigkeiten für die Simulation von RAAMs erweitert.

<p>Connectionist models have been criticized for not being able to form compositional representations of recursive data structures such as trees and lists, a matter that has been addressed by models as Elman networks, RAAM and B-RAAM. These architectures seem to have common features with the human short-term memory regarding recall. Both show a strong recency effect; however, the human memory also exhibits a primacy effect due to rehearsal. The problem is that the connectionist models do not have the primacy aspect, which complicates the learning of long-term dependencies. A long-term dependency is when items presented early should affect the behaviour of the model. Learning long-term dependencies is a problem that is hard to address within these architectures.</p><p>Delay-lines might be used as a mechanism for implementing rehearsal within connectionist models. However, it has not been clarified how the use of delay-lines affects the recency and the primacy aspect. In this thesis, delay-lines are introduced in B-RAAM. This study investigates how the primacy and the recency aspect are affected by the use of delay-lines, aiming to improve the ability to identify long-term dependencies. The results show that by using delay-lines, B-RAAM has both primacy and recency.</p>

Connectionist models have been criticized for not being able to form compositional representations of recursive data structures such as trees and lists, a matter that has been addressed by models as Elman networks, RAAM and B-RAAM. These architectures seem to have common features with the human short-term memory regarding recall. Both show a strong recency effect; however, the human memory also exhibits a primacy effect due to rehearsal. The problem is that the connectionist models do not have the primacy aspect, which complicates the learning of long-term dependencies. A long-term dependency is when items presented early should affect the behaviour of the model. Learning long-term dependencies is a problem that is hard to address within these architectures. Delay-lines might be used as a mechanism for implementing rehearsal within connectionist models. However, it has not been clarified how the use of delay-lines affects the recency and the primacy aspect. In this thesis, delay-lines are introduced in B-RAAM. This study investigates how the primacy and the recency aspect are affected by the use of delay-lines, aiming to improve the ability to identify long-term dependencies. The results show that by using delay-lines, B-RAAM has both primacy and recency.

Rekursive Auto-Assoziative Speicher (RAAM) sind spezielle Neuronale Netze (NN), die in der Lage sind, hierarchiche Strukturen zu verarbeiten. Bei der Simulation dieser Netze gibt es einige Besonderheiten, wie z.B. die dynamische Trainingsmenge, zu beachten. In der Arbeit werden diese und die daraus resultierenden angepassten Lernalgorithmen erörtert. Außerdem wird ein normaler Backpropagation-Simulator (Xerion) um die Fähigkeiten für die Simulation von RAAMs erweitert.

Stridsvagnsminan har varit ett vapen sedan 1916 och därefter har minan tillfört ytterligare en dimension till striden. Minorna har utvecklats under åren och blivit mer avancerade och fler system har möjlighet att placera ut minor. Understödsbehov och framtida bekämpningskoncept, en rapport från Artilleriets stridsskola, visade att avståndslagda minor är ett vapensystem som kan användas i striden för att bekämpa fordon. Försvarsmakten planerar att införskaffa ett system som har kapacitet till avståndslagd minering. Syftet med studien var att beskriva hur användningen av fordonsåverkande avståndslagda minor kan påverkas då de används i strid i olika svenska terrängtyper. Fem officerare från olika delar av Sverige intervjuades där de fick beskriva hur avståndslaga minor kunde användas i strid. Därefter analyserades svaren utifrån två scenarion, Skåne respektive Norrland, som representerar öppen terräng respektive betäckt terräng i syfte att se hur avståndslagda minors användning påverkar teorin om kombinerade vapen med en mekaniserad skyttebataljon. Analysen av de två scenariona visade att det blev svårare att uppnå kombinerade vapen i öppen terräng jämfört med betäckt terräng på grund av god framkomlighet och brist på kanaliserad terräng. Vilket skulle innebära att fler avståndslagda minorna skulle behövas för att påverka en motståndare i öppen terräng.