Goal reasoning for autonomous agents using automated planning

Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely a plan, which achieves a goal from a given initial state. Most planning research consider goals are provided by a external user, and agents just have to find a plan to achieve them. However, there exist many real world domains where agents should not only reason about their actions but also about their goals, generating new ones or changing them according to the perceived environment. In this thesis we aim at broadening the goal reasoning capabilities of planningbased agents, both when acting in isolation and when operating in the same environment as other agents. In single-agent settings, we firstly explore a special type of planning tasks where we aim at discovering states that fulfill certain cost-based requirements with respect to a given set of goals. By computing these states, agents are able to solve interesting tasks such as find escape plans that move agents in to safe places, hide their true goal to a potential observer, or anticipate dynamically arriving goals. We also show how learning the environment’s dynamics may help agents to solve some of these tasks. Experimental results show that these states can be quickly found in practice, making agents able to solve new planning tasks and helping them in solving some existing ones. In multi-agent settings, we study the automated generation of goals based on other agents’ behavior. We focus on competitive scenarios, where we are interested in computing counterplans that prevent opponents from achieving their goals. We frame these tasks as counterplanning, providing theoretical properties of the counterplans that solve them. We also show how agents can benefit from computing some of the states we propose in the single-agent setting to anticipate their opponent’s movements, thus increasing the odds of blocking them. Experimental results show how counterplans can be found in different environments ranging from competitive planning domains to real-time strategy games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert

Rejection and online learning with prototype-based classifiers in adaptive metrical spaces

Fischer L. Rejection and online learning with prototype-based classifiers in adaptive metrical spaces. Bielefeld: Universität Bielefeld; 2016.The rising amount of digital data, which is available in almost every domain, causes the need for intelligent, automated data processing. Classification models constitute particularly popular techniques from the machine learning domain with applications ranging from fraud detection up to advanced image classification tasks. Within this thesis, we will focus on so-called prototype-based classifiers as one prominent family of classifiers, since they offer a simple classification scheme, interpretability of the model in terms of prototypes, and good generalisation performance. We will face a few crucial questions which arise whenever such classifiers are used in real-life scenarios which require robustness and reliability of classification and the ability to deal with complex and possibly streaming data sets. Particularly, we will address the following problems: - Deterministic prototype-based classifiers deliver a class label, but no confidence of the classification. The latter is particularly relevant whenever the costs of an error are higher than the costs to reject an example, e.g. in a safety critical system. We investigate ways to enhance prototype-based classifiers by a certainty measure which can efficiently be computed based on the given classifier only and which can be used to reject an unclear classification. - For an efficient rejection, the choice of a suitable threshold is crucial. We investigate in which situations the performance of local rejection can surpass the choice of only a global one, and we propose efficient schemes how to optimally compute local thresholds on a given training set. - For complex data and lifelong learning, the required classifier complexity can be unknown a priori. We propose an efficient, incremental scheme which adjusts the model complexity of a prototype-based classifier based on the certainty of the classification. Thereby, we put particular emphasis on the question how to adjust prototype locations and metric parameters, and how to insert and/or delete prototypes in an efficient way. - As an alternative to the previous solution, we investigate a hybrid architecture which combines an offline classifier with an online classifier based on their certainty values, thus directly addressing the stability/plasticity dilemma. While this is straightforward for classical prototype-based schemes, it poses some challenges as soon as metric learning is integrated into the scheme due to the different inherent data representations. - Finally, we investigate the performance of the proposed hybrid prototype-based classifier within a realistic visual road-terrain-detection scenario

Information Reliability on the Social Web - Models and Applications in Intelligent User Interfaces

The Social Web is undergoing continued evolution, changing the paradigm of information production, processing and sharing. Information sources have shifted from institutions to individual users, vastly increasing the amount of information available online. To overcome the information overload problem, modern filtering algorithms have enabled people to find relevant information in efficient ways. However, noisy, false and otherwise useless information remains a problem. We believe that the concept of information reliability needs to be considered along with information relevance to adapt filtering algorithms to today's Social Web. This approach helps to improve information search and discovery and can also improve user experience by communicating aspects of information reliability.This thesis first shows the results of a cross-disciplinary study into perceived reliability by reporting on a novel user experiment. This is followed by a discussion of modeling, validating, and communicating information reliability, including its various definitions across disciplines. A selection of important reliability attributes such as source credibility, competence, influence and timeliness are examined through different case studies. Results show that perceived reliability of information can vary greatly across contexts. Finally, recent studies on visual analytics, including algorithm explanations and interactive interfaces are discussed with respect to their impact on the perception of information reliability in a range of application domains

Platform for decoupling experience managers and environments

Experience Management employs Artificial Intelligence technologies to enhance people's interactive application experiences by dynamically modifying the environment during the experience. In game-related research, there is a prevailing trend where each experience manager is tightly integrated with the specific environment it can manipulate. This integration poses a challenge in comparing different managers within a single environment or a single manager across multiple environments. In this dissertation, I propose a solution to address this issue by introducing EM-Glue, an intermediary software platform that decouples experience managers from the environments they can modify. Prior to presenting the solution, I provide a comprehensive problem description and conduct a literature review to explore the current state of the field. Subsequently, I outline the platform's structural design, including a communication protocol facilitating interaction between managers and environments, as well as the regular communication process. Additionally, I develop a use case to evaluate the effectiveness of the proposed solution. This involves employing an environment and two experience managers: the Camelot Wrapper, a software I constructed to extend the interactive visualization engine Camelot and connect it to the platform, PaSSAGE, an existing experience manager adapted for use with the platform, and a random experience manager. The evaluation results demonstrate the platform's ability to decouple experience managers from environments, enabling future work to compare experience managers across multiple environments