The development of the research "Goal Formulation in Intelligent Agents" is anchored on the rationale that to be truly called "intelligent", an agent must not only be capable of knowing how to achieve its given goals; preferably, it must also have the capability to formulate its own goals. It must be able to detect its own goals, assess their feasibility, prioritize them, evaluate their validity as to whether they have to be acted upon, terminated, or suspended. This research has developed and implemented an intelligent system that is capable for formulating its own goals. Goal formulation refers to the intelligent behavior that an agent exhibits when reasoning about what goals to pursue and when to pursue them. It is an integrated reasoning mechanism that identifies the relevant goals that an agent needs to accomplish to affect the external world (Goal detection); constantly updates the qualitative and quantitative information attributed to the active goals as events unfold (Active goal status evaluation); assesses whether a goal is attainable through the application of the agent's own actions (Goal achievability assessment); and dynamically evaluates the relative merits of an agent's tasks, provides the agent with a sound basis to make a rational choice among a set of competing alternatives and then decides what to do next based on the choice made (Next action selection). In the development of the goal formulator, the types and structure of the required knowledge are identified; architectures for the various goal formulation components have been designed; and algorithms for the various goal formulation reasoning mechanisms (e.g. application of NPV economic decision criterion) have been developed and implemented in Prolog. To prove the applicability of the goal formulation concepts that this research had developed, the system was applied in the housekeeping domain. Simulations of some housekeeping cases are provided.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / This paper describes a short distance telemetry network which measures and relays time, space, and position information among a group of ground vehicles. The goal is to allow a lead vehicle to be under human control, or perhaps controlled using advanced autonomous path planning and navigation tools. The telemetry network will then allow a series of inexpensive, unmanned vehicles to follow the lead vehicle at a safe distance. Ultrasonic and infrared signals will be relayed between the vehicles, to allow the following vehicles to locate their position, and track the lead vehicle.

published_or_final_version / Law / Doctoral / Doctor of Philosophy

published_or_final_version / Earth Sciences / Master / Master of Philosophy

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This work presents two approaches for integrating data from a low cost GPS receiver in a Monte Carlo localization algorithm. Firstly, an easily applicable method based on data in the standard NMEA protocol is shown. Secondly, an original algorithm utilizing lower level pseudorange measurements accessed in binary receiver-specific format is presented. In addition, a set of tools for analysis of GPS measurement errors on receivers with SiRF III chipset was implemented

<p>In this work new artificial learning and innate control mechanisms are proposed for application</p><p>in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots)</p><p>existent in the literature is enhanced with respect to its capacity of exploring the environment and</p><p>avoiding risky configurations (that lead to collisions with obstacles even after learning). The</p><p>particular autonomous system is based on modular hierarchical neural networks. Initially,the</p><p>autonomous system does not have any knowledge suitable for exploring the environment (and</p><p>capture targets œ foraging). After a period of learning,the system generates efficientobstacle</p><p>avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous</p><p>system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky</p><p>configurations) are discussed and the new learning and controltechniques (applied to the</p><p>autonomous system) are verified through simulations. It is shown the effectiveness of the</p><p>proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and</p><p>decrease their probability of appearance in the future and the number of collisions in risky</p><p>situations is significantly decreased. Experiments also consider maze environments (with targets</p><p>distant from each other) and dynamic environments (with moving objects).</p>

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the literature is enhanced with respect to its capacity of exploring the environment and avoiding risky configurations (that lead to collisions with obstacles even after learning). The particular autonomous system is based on modular hierarchical neural networks. Initially,the autonomous system does not have any knowledge suitable for exploring the environment (and capture targets œ foraging). After a period of learning,the system generates efficientobstacle avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky configurations) are discussed and the new learning and controltechniques (applied to the autonomous system) are verified through simulations. It is shown the effectiveness of the proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and decrease their probability of appearance in the future and the number of collisions in risky situations is significantly decreased. Experiments also consider maze environments (with targets distant from each other) and dynamic environments (with moving objects).