Software tools for the cognitive development of autonomous robots

Robotic systems are evolving towards higher degrees of autonomy. This paper reviews the cognitive tools available nowadays for the fulfilment of abstract or long-term goals as well as for learning and modifying their behaviour.Peer ReviewedPostprint (author's final draft

Perpetual requirements engineering

This dissertation attempts to make a contribution within the fields of distributed systems, security, and formal verification. We provide a way to formally assess the impact of a given change in three different contexts. We have developed a logic based on Lewis’s Counterfactual Logic. First we show how our approach is applied to a standard sequential programming setting. Then, we show how a modified version of the logic can be used in the context of reactive systems and sensor networks. Last but not least we show how this logic can be used in the context of security systems. Traditionally, change impact analysis has been viewed as an area in traditional software engineering. Software artifacts (source code, usually) are modified in response to a change in user requirements. Aside from making sure that the changes are inherently correct (testing and verification), programmers (software engineers) need to make sure that the introduced changes are coherent with those parts of the systems that were not affected by the artifact modification. The latter is generally achieved by establishing a dependency relation between software artifacts. In rough lines, the process of change management consists of projecting the transitive closure of the this dependency relation based on the set of artifacts that have actually changed and assessing how the related artifacts changed. The latter description of the traditional change management process generally occurs after the affected artifacts are changed. Undesired secondary effects are usually found during the testing phase after the changes have been incorporated. In cases when there is certain level of criticality, there is always a division between production and development environments. Change management (either automatic, tool driven, or completely manually done) can introduce extraneous defects into any of the changed software life-cycle artifacts. The testing phase tries to eradicate a relatively large portion of the undesired defects introduced by change. However, traditional testing techniques are limited by their coverage strength. Therefore, even when maximum coverage is guaranteed there is always the non-zero probability of having secondary effects prior to a change

Graphical Models and Symmetries : Loopy Belief Propagation Approaches

Whenever a person or an automated system has to reason in uncertain domains, probability theory is necessary. Probabilistic graphical models allow us to build statistical models that capture complex dependencies between random variables. Inference in these models, however, can easily become intractable. Typical ways to address this scaling issue are inference by approximate message-passing, stochastic gradients, and MapReduce, among others. Exploiting the symmetries of graphical models, however, has not yet been considered for scaling statistical machine learning applications. One instance of graphical models that are inherently symmetric are statistical relational models. These have recently gained attraction within the machine learning and AI communities and combine probability theory with first-order logic, thereby allowing for an efficient representation of structured relational domains. The provided formalisms to compactly represent complex real-world domains enable us to effectively describe large problem instances. Inference within and training of graphical models, however, have not been able to keep pace with the increased representational power. This thesis tackles two major aspects of graphical models and shows that both inference and training can indeed benefit from exploiting symmetries. It first deals with efficient inference exploiting symmetries in graphical models for various query types. We introduce lifted loopy belief propagation (lifted LBP), the first lifted parallel inference approach for relational as well as propositional graphical models. Lifted LBP can effectively speed up marginal inference, but cannot straightforwardly be applied to other types of queries. Thus we also demonstrate efficient lifted algorithms for MAP inference and higher order marginals, as well as the efficient handling of multiple inference tasks. Then we turn to the training of graphical models and introduce the first lifted online training for relational models. Our training procedure and the MapReduce lifting for loopy belief propagation combine lifting with the traditional statistical approaches to scaling, thereby bridging the gap between statistical relational learning and traditional statistical machine learning