We consider problems of sequential robot manipulation (aka. combined task and motion planning) where the objective is primarily given in terms of a cost function over the final geometric state, rather than a symbolic goal description. In this case we should leverage optimization methods to inform search over potential action sequences. We propose to formulate the problem holistically as a 1st-order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. We tackle the challenge of solving such programs by proposing three levels of approximation: The coarsest level introduces the concept of the effective end state kinematics, parametrically describing all possible end state configurations conditional to a given symbolic action sequence. Optimization on this level is fast and can inform symbolic search. The other two levels optimize over interaction keyframes and eventually over the full world trajectory across interactions. We demonstrate the approach on a problem of maximizing the height of a physically stable construction from an assortment of boards, cylinders and blocks.

Rosetta is a European Space Agency (ESA) cornerstone mission that entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014 and will escort the comet for a 1.5 year nominal mission offering the most detailed study of a comet ever undertaken by humankind. The Rosetta orbiter has 11 scientific instruments (4 remote sensing) and the Philae lander to make complementary measurements of the comet nucleus, coma (gas and dust), and surrounding environment. The ESA Rosetta Science Ground Segment has developed a science scheduling system that includes an automated scheduling capability to assist in developing science plans for the Rosetta Orbiter. While automated scheduling is a small portion of the overall Science Ground Segment (SGS) as well as the overall scheduling system, this paper focuses on the automated and semi-automated scheduling software (called ASPEN-RSSC) and how this software is used.

The last years have seen an exponential increase in the interest for the development of methods for combining probability with Description Logics (DLs). These methods are very useful to model real world domains, where incompleteness and uncertainty are common. This combination has become a fundamental component of the Semantic Web.Our work started with the development of a probabilistic semantics for DL, called DISPONTE, that applies the distribution semantics to DLs. Under DISPONTE we annotate axioms of a theory with a probability, that can be interpreted as the degree of our belief in the corresponding axiom, and we assume that each axiom is independent of the others. Several algorithms have been proposed for supporting the development of the Semantic Web. Efficient DL reasoners, such us Pellet, are able to extract implicit information from the modeled ontologies. Despite the availability of many DL reasoners, the number of probabilistic reasoners is quite small. We developed BUNDLE, a reasoner based on Pellet that allows to compute the probability of queries. BUNDLE, like most DL reasoners, exploits an imperative language for implementing its reasoning algorithm. Nonetheless, usually reasoning algorithms use non-deterministic operators for doing inference. One of the most used approaches for doing reasoning is the tableau algorithm which applies a set of consistency preserving expansion rules to an ABox, but some of these rules are non-deterministic.In order to manage this non-determinism, we developed the system TRILL which performs inference over DISPONTE DLs. It implements the tableau algorithm in the declarative Prolog language, whose search strategy is exploited for taking into account the non-determinism of the reasoning process. Moreover, we developed a second version of TRILL, called TRILL^P, which implements some optimizations for reducing the running time. The parameters of probabilistic KBs are difficult to set. It is thus necessary to develop systems which automatically learn this parameters starting from the information available in the KB. We presented EDGE that learns the parameters of a DISPONTE KB, and LEAP, that learn the structure together with the parameters of a DISPONTE KB. The main objective is to apply the developed algorithms to Big Data. Nonetheless, the size of the data requires the implementation of algorithms able to handle it. It is thus necessary to exploit approaches based on the parallelization and on cloud computing. Nowadays, we are working to improve EDGE and LEAP in order to parallelize them.

Autonomous agents operating in a dynamic environment must be able to reason and make decisions about actions in pursuit of their goals. In addition, in a normative environment an agent's actions are not only directed by the agent's goals, but also by the norms imposed on the agent. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent's practical attitude such as goals, plans and norms. In this thesis we aim: (i) to introduce a model for normative practical reasoning that allows the agents to plan for multiple and potentially conflicting goals and norms at the same time (ii) to implement the model both formally and computationally, (iii) to identify the best plan for the agent to execute by means of argumentation framework and grounded semantics, (iv) to justify the best plan via argumentation-based persuasion dialogue for grounded semantics.

In decision-theoretic planning problems, such as (partially observable) Markov decision problems or coordination graphs, agents typically aim to optimize a scalar value function. However, in many real-world problems agents are faced with multiple possibly conflicting objectives. In such multi-objective problems, the value is a vector rather than a scalar, and we need methods that compute a coverage set, i.e., a set of solutions optimal for all possible trade-offs between the objectives. In this project propose new multi-objective planning methods that compute the so-called convex coverage set (CCS): the coverage set for when policies can be stochastic, or the preferences are linear. We show that the CCS has favorable mathematical properties, and is typically much easier to compute that the Pareto front, which is often axiomatically assumed as the solution set for multi-objective decision problems.

Earth observation from space allows us to better understand natural phenomenas such as marine currents, to prevent or follow natural disasters, to follow climate evolution and many other things. To achieve that, there are a great number of artificial satellites orbiting Earth, equipped with high-resolution optical instruments and communicating with a network of ground stations. A satellite is said to be agile when it is able to move quickly around its gravity center along its three axes while moving along its orbit, thanks to gyroscopic actuators. It is equipped with a body-mounted optical instrument. To observe a ground area with the instrument, the satellite must be pointed to it. In practice, users submit observation requests to a mission center, which builds activity plans which are sent to the satellites. These plans contain several types of actions such as orbital maneuvers, acquisition realisations and acquisition downloads towards ground stations. Many techniques are used to synthesize such activity plans. Until now, plans are computed offline on the ground and converted into telecommands that the satellite executes strictly, without any flexibility. However, the satellite evolves in a dynamic environment. Unexpected events occur, such as meteorological changes or new urgent observation requests, that the system must handle. Moreover, resource consumption is not always well known. Until now, to ensure that plans will be executable on board with these uncertainties, they are built with worst-case hypothesis on resources consumption. The objective of this work is to give more autonomy to the satellite without compromising the predictability that is needed for some activities. On the ground, we have high computing power and high uncertainty, while on board we have very low computing power and low uncertainty. The main idea is to share decision-making between ground and board to take advantage of the high computing power on the ground and of the low uncertainty on board. First we apply this idea to download scheduling which consists in scheduling file downloads during ground station visibility windows. Second, we apply this idea to observation planning.

Abstract argumentation frameworks (AFs) are one of the central formalisms in AI; equipped with a wide range of semantics, they have proven useful in several application domains. In the thesis we want to complete and extend the recent study on expressiveness of argumentation semantics and use these and other theoretical results for implementations of reasoning tasks in AFs. Moreover, we plan to utilize results on realizability in dynamic scenarios of abstract argumentation, such as revision of argumentation frameworks. Hereby, the knowledge of which extensions can occur together is of central interest when trying to achieve a certain outcome. In other words, the ultimate goal of the thesis is to gain theoretical insights on argumentation semantics in order to employ them in practically efficient reasoning systems for both the evaluation and evolution of AFs.

Increasing complexity of technical systems requires a precise fault localization in order to reduce maintenance costs and system downtimes. Model-based diagnosis has been presented as a method to derive root causes for observed symptoms, utilizing a description of the system to be diagnosed. Practical applications of model-based diagnosis, however, are often prevented by the initial modeling task and computational complexity associated with diagnosis. In the proposed thesis, we investigate techniques addressing these issues. In particular, we utilize a mapping function which converts fault information available in practice into propositional horn logic sentences to be used in abductive model-based diagnosis. Further, we plan on devising algorithms which allow an efficient computation of explanations given the obtained models.

In this dissertation, we propose an online learning technique to predict a value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions including (a) changing the quality of data streams, (b) addition or deletion of data streams (ii) integrating the results of several analysis algorithms for each data source when the most suitable algorithm for a given data source is not known a priori (iii) dynamically weighting the prediction of each analysis algorithm and data source on the system prediction based on their varying quality.

Static analysis is a core task in query optimization and knowledge base verification. We study static analysis techniques for SPARQL, the standard language for querying Semantic Web data. Specifically, we investigate the query containment problem and query-update independence analysis. We are interested in developing techniques through reductions to the validity problem in logic.