Qualitative spatial representation and reasoning: A hierarchical approach

The ability to reason in space is crucial for agents in order to make informed decisions. Current high-level qualitative approaches to spatial reasoning have serious deficiencies in not reflecting the hierarchical nature of spatial data and human spatial cognition. This article proposes a framework for hierarchical representation and reasoning about topological information, where a continuous model of space is approximated by a collection of discrete sub-models, and spatial information is hierarchically represented in discrete sub-models in a rough set manner. The work is based on the Generalized Region Connection Calculus theory, where continuous and discrete models of space are coped in a unified way. Reasoning issues such as determining the mereological (part-whole) relations between two rough regions are also discussed. Moreover, we consider an important problem that is closely related to map generalization in cartography and Geographical Information Science. Given a spatial configuration at a finer level, we show how to construct a configuration at a coarser level while preserving the mereological relations. © The Author 2007. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved

The FF Planning System: Fast Plan Generation Through Heuristic Search

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP

On the Complexity of Case-Based Planning

We analyze the computational complexity of problems related to case-based planning: planning when a plan for a similar instance is known, and planning from a library of plans. We prove that planning from a single case has the same complexity than generative planning (i.e., planning "from scratch"); using an extended definition of cases, complexity is reduced if the domain stored in the case is similar to the one to search plans for. Planning from a library of cases is shown to have the same complexity. In both cases, the complexity of planning remains, in the worst case, PSPACE-complete

Reformulation in planning

Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning

Discrimination of Semi-Quantitative Models by Experiment Selection: Method and Application in Population Biology

Modeling an experimental system often results in a number of alternative models that are justified equally well by the experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for the discrimination of models in the form of semiquantitative differential equations. The method is a generalization of previous work in model discrimination. It is based on an entropy criterion for the selection of the most informative experiment which can handle cases where the models predict multiple qualitative behaviors. The applicability of the method is demonstrated on a real-life example, the discrimination of a set of competing models of the growth of phytoplankton in a bioreactor

A Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications

Taking as input natural images and videos augmented reality (AR) applications aim to enhance the real world with superimposed digital contents enabling interaction between the user and the environment. One important step in this process is automatic scene analysis and understanding that should be performed both in real time and with a good level of object recognition accuracy. In this work an end-to-end framework based on the combination of a Super Resolution network with a detection and recognition deep network has been proposed to increase performance and lower processing time. This novel approach has been evaluated on two different datasets: the popular COCO dataset whose real images are used for benchmarking many different computer vision tasks, and a generated dataset with synthetic images recreating a variety of environmental, lighting and acquisition conditions. The evaluation analysis is focused on small objects, which are more challenging to be correctly detected and recognised. The results show that the Average Precision is higher for smaller and low resolution objects for the proposed end-to-end approach in most of the selected conditions

Competing effects of spreading rate, crystal fractionation and source variability on Fe isotope systematics in mid-ocean ridge lavas

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Richter, M., Nebel, O., Schwindinger, M., Nebel-Jacobsen, Y., & Dick, H. J. B. Competing effects of spreading rate, crystal fractionation and source variability on Fe isotope systematics in mid-ocean ridge lavas. Scientific Reports, 11(1), (2021): 4123, https://doi.org/10.1038/s41598-021-83387-7.Two-thirds of the Earth is covered by mid-ocean ridge basalts, which form along a network of divergent plate margins. Basalts along these margins display a chemical diversity, which is consequent to a complex interplay of partial mantle melting in the upper mantle and magmatic differentiation processes in lower crustal levels. Igneous differentiation (crystal fractionation, partial melting) and source heterogeneity, in general, are key drivers creating variable chemistry in mid-ocean ridge basalts. This variability is reflected in iron isotope systematics (expressed as δ57Fe), showing a total range of 0.2 ‰ from δ57Fe =  + 0.05 to + 0.25 ‰. Respective contributions of source heterogeneity and magma differentiation leading to this diversity, however, remain elusive. This study investigates the iron isotope systematics in basalts from the ultraslow spreading Gakkel Ridge in the Arctic Ocean and compares them to existing data from the fast spreading East Pacific Rise ridge. Results indicate that Gakkel lavas are driven to heavier iron isotope compositions through partial melting processes, whereas effects of igneous differentiation are minor. This is in stark contrast to fast spreading ridges showing reversed effects of near negligible partial melting effects followed by large isotope fractionation along the liquid line of descent. Gakkel lavas further reveal mantle heterogeneity that is superimposed on the igneous differentiation effects, showing that upper mantle Fe isotope heterogeneity can be transmitted into erupting basalts in the absence of homogenisation processes in sub-oceanic magma chambers.This work was supported by an ARC grant FT140101062 to O.N. H.J.B.D was supported by the NSF grants PLR 9912162, PLR 0327591, OCE 0930487 and OCE 1434452

Existence of Monetary Steady States in a Matching Model: Indivisible Money

Existence of a monetary steady state is established for a random matching model with divisible goods, indivisible money, and take-it-or-leave-it offers by consumers. There is no restriction on individual money holdings. The background environment is that in papers by Shi and by Trejos and Wright. The monetary steady state shown to exist has nice properties: the value function, defined on money holdings, is increasing and strictly concave, and the measure over money holdings has full support.

Planning When Goals Change: A Moving Target Search Approach

International audienceDevising intelligent robots or agents that interact with humans is a major challenge for artificial intelligence. In such contexts, agents must constantly adapt their decisions according to human activities and modify their goals. In this paper, we tackle this problem by introducing a novel planning approach, called Moving Goal Planning (MGP), to adapt plans to goal evolutions. This planning algorithm draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible triggering new searches when the goal changes over time. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to update incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach