This dissertation investigates the fronto-parietal representation of the structure of organised mental episodes by studying its effect on the representation of cognitive events occurring at various positions within it. The experiments in chapter 2 look at the completion of hierarchically organized mental (task/subtask) episodes. Multiple identical target-detection events were organized into a sequential task episode, and the individual events were connected in a means-to-end relationship. It is shown that events that are conceptualized as completing defined task episodes elicit greater activity compared to identical events lying within the episode; the magnitude of the end of episode activity depended on the hierarchical abstraction of the episode. In chapter 3, the effect of ordinal position of the cognitive events, making up the task episode, on their representation is investigated in the context of a biphasic task episode. The design further manipulated the cognitive load of the two phases independently. This allowed for a direct comparison of the effect of phase vis-à-vis the effect of cognitive load. The results showed that fronto-parietal regions that increased their activity in response to cognitive load, also increased their activity for the later phases of the task episode, even though the cognitive load associated with the later phase was, arguably, lower than the previous phase. Chapter 4 investigates if the characteristics of the higher-level representations, like organization of task descriptions, have a causal role in determining the structure of the ensuing mental episode. Results show this to be true. They also confirm the results of earlier chapters in a different framework. Chapter 5 shows that the effect of episode structure is not limited to the elicited activity, but also affects the information content of the representation of the events composing the episode. Specifically, the information content in many regions of later steps is higher than that of earlier steps. Together, the results show widespread representation of the structure of organised mental episodes.

In agent based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainity and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle these uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task using an environment, which has randomly placed obstacles and a goal position to simulate an environment similar to an autonomous robot&rsquo / s indoor environment. Then the control system was extended to control an agent in a multi-agent environment. The main motivation of this study is to design a control system which is robust to errors and easy to modify. Behaviour based approach with the advantages of fuzzy reasoning systems is used in the system.

Programming a complex robot is difficult, time-consuming and expensive. Learning from Demonstration (LfD) is a methodology where a teacher demonst--rates a task and the robot learns to execute the task. This thesis presents a method which generates reactive robot behaviour learned from demonstration where sequences of action are implicitly coded in a rule-based manner. It also presents a novel approach to find behaviour hierarchy among behaviours of a demonstration.In the thesis, the system learns the activation rule of primitives as well as the association that should be performed between sensor and motor primitives. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. The underlying rule for the activation of associations is learned using a neural network from demonstrated data. Behaviour hierarchy among different sensor-motor associations is learnt using heuristic logic minimization technique called espresso algorithm. Once relationship among the associations is learnt, all the logical relationships are used to generate a hierarchical tree of behaviours using a novel approach that is proposed in the thesis. This allows us to represent the behaviour in hierarchical way as a set of associations between sensor and motor primitives in a readable script which is deployed on Playful.The method is tested on a simulation by varying the number of targets, showing that the system learns underlying rules for sensor-motor association providing high F1-score for each association. It is also shown by changing the complexity of simulation that the system generalises the solution and the knowledge learnt from a sensor-motor association is transferable with all the instances of that association. / Att programmera en komplex robot är svårt, tidskrävande och dyrt. Learning from Demonstration (LfD) är en metod där en lärare visar en uppgift och roboten lär sig att utföra uppgiften. Denna avhandling presenterar en metod som genererar reaktivt robotbeteende lärt från demonstration där handlingssek--venser implicit kodas på ett regelbaserat sätt. Den presenterar också ett nytt tillvägagån- -gssätt för att hitta beteendeshierarki bland beteenden i en demonstration.I avhandlingen lär sig systemet aktiveringsregeln för primitiva såväl som sambandet som ska utföras mellan sensor och motor primitives. För att göra det använder vi det lekfulla programmeringsspråket som bygger på reaktivt programmeringsparadigm. Den underliggande regeln för aktivering av föreningar lärs med hjälp av ett neuralt nätverk från demonstrerade data. Beteendeshierarki mellan olika sensor-motorföreningar lärs med hjälp av heuristisk logikminimeringsteknik som kallas espressoalgoritm. När förhållandet mellan föreningarna har lärt sig används alla logiska förhållanden för att generera ett hierarkiskt beteendeträd med den nya metoden som föreslås i avhandlingen. Detta gör att vi kan representera beteendet på hierarkiskt sätt som en uppsättning associeringar mellan sensor och motorprimitiv i ett läsbart skript som används på lekfull.Metoden testas på en simulering genom att variera antalet mål, vilket visar att systemet lär sig underliggande regler för sensor-motorassociation som ger hög F1-poäng för varje association. Det visas också genom att ändra komplexiteten i simuleringen att systemet generaliserar lösningen och kunskapen som lärts från en sensor-motorförening är överförbar med alla förekomster av den associeringen.