Initial State Stabilities and Inverse Engineering in Conflict Resolution

Two original contributions are made which extend the Graph Model for Conflict Resolution: one is a new family of solution concepts, while the other is a novel methodological approach. In addition to theoretical contributions, applications to complex energy problems are demonstrated; in particular, the consideration of the ongoing Trans Mountain Expansion Project is the first of its kind. The family of solution concepts, called initial state stabilities, is designed to complement existing solution concepts within the Graph Model framework by modelling both risk-averse and risk-seeking decision-makers. The comparison which underpins these concepts examines the consequences of moving from a given starting state to those of remaining in that state. The types of individuals modelled by these stability concepts represent a new class of decision-makers which, up until now, had not been considered in the Graph Model paradigm. The innovative methodology presented is designed to "inverse engineer" decision-makers’ preferences based on their observable behaviour. The algorithms underlying the inverse engineering methodology are based on the most commonly used stability concepts in the Graph Model for Conflict Resolution and function by reducing the set of possible preference rankings for each decision-maker. The reduction is based on observable moves and counter-moves made by decision-makers. This procedure assists stakeholders in optimizing their own decision-making process based on information gathered about their opponents and can also be used to improve the modelling of strategic interactions. In addition to providing decision-makers and analysts with up-to-date preference information about opponents, the methodology is also equipped with an ADVICE function which enriches the decision-making process by providing important information regarding potential moves. Decision-makers who use the methods introduced in this thesis are provided with the expected value of each of their possible moves, with the probability of the opponent’s next response, and with the opponent reachable states. This insightful data helps establish an accurate picture of the conflict situation and in so doing, aids stakeholders in making strategic decisions. The applicability of this methodology is demonstrated through the study of the conflict surrounding the Trans Mountain Expansion Project in British Columbia, Canada

Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.Comment: 15 pages, 12 figure

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie

Detecting Functional Requirements Inconsistencies within Multi-teams Projects Framed into a Model-based Web Methodology

One of the most essential processes within the software project life cycle is the REP (Requirements Engineering Process) because it allows specifying the software product requirements. This specification should be as consistent as possible because it allows estimating in a suitable manner the effort required to obtain the final product. REP is complex in itself, but this complexity is greatly increased in big, distributed and heterogeneous projects with multiple analyst teams and high integration between functional modules. This paper presents an approach for the systematic conciliation of functional requirements in big projects dealing with a web model-based approach and how this approach may be implemented in the context of the NDT (Navigational Development Techniques): a web methodology. This paper also describes the empirical evaluation in the CALIPSOneo project by analyzing the improvements obtained with our approach.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Automating Fine Concurrency Control in Object-Oriented Databases

Several propositions were done to provide adapted concurrency control to object-oriented databases. However, most of these proposals miss the fact that considering solely read and write access modes on instances may lead to less parallelism than in relational databases! This paper cope with that issue, and advantages are numerous: (1) commutativity of methods is determined a priori and automatically by the compiler, without measurable overhead, (2) run-time checking of commutativity is as efficient as for compatibility, (3) inverse operations need not be specified for recovery, (4) this scheme does not preclude more sophisticated approaches, and, last but not least, (5) relational and object-oriented concurrency control schemes with read and write access modes are subsumed under this proposition

Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter

We consider the problem of conditioning a geological process-based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterize the posterior probability distribution of the geological quantities of interest by using a variant of the ensemble Kalman filter, an estimation method which linearly and sequentially conditions realisations of the system state to data. A test case involving synthetic data is used to assess the performance of the proposed estimation method, and to compare it with similar approaches. We further apply the method to a more realistic test case, involving real well data from the Colville foreland basin, North Slope, Alaska.Comment: 34 pages, 10 figures, 4 table

Symmetry-breaking Answer Set Solving

In the context of Answer Set Programming, this paper investigates symmetry-breaking to eliminate symmetric parts of the search space and, thereby, simplify the solution process. We propose a reduction of disjunctive logic programs to a coloured digraph such that permutational symmetries can be constructed from graph automorphisms. Symmetries are then broken by introducing symmetry-breaking constraints. For this purpose, we formulate a preprocessor that integrates a graph automorphism system. Experiments demonstrate its computational impact.Comment: Proceedings of ICLP'10 Workshop on Answer Set Programming and Other Computing Paradig