This paper presents a bottom-up approach to machine ethics, based on the Measurement Logic Machine (MLM). It is explained how ethical notions emerge from the workings, architecture, and environmental assumptions of the MLM framework. The MLM uses sequences of measurements to perform short-term predictive inference. The MLM ethical behavior stems from the inner evaluation of measurements that are used to filter the predictions. The MLM ethical discernment is based on measurements that detect immediate suffering in other agents. Also, a definition of what is an ethically positive modification of the inner evaluations is proposed, based on the notion of environmental intelligence and the corresponding notion of suffering. It is shown how this double approach is consistent with our intuitive notion of ethics. The MLM, with or without ethical discernment, can be used in evolutionary game theory, and gives clues to the search of ethical senses that increase the chances of survival of autonomous agents.

Volcanoes are a principal factor of hazard across the Pacific Rim, with their focus of interest mostly divided into pyroclastic flows and ash deposition. The latter has significantly more impact due to its widespread geographical reach and prolonged effects in human activities and health. TEPHRA is a volcanic ash dispersion model based on a simple version of the advection-diffusion Suzuki model, which has been revisited and modified for the Iraz\'u volcano in Costa Rica. A full parameter exploration is necessary in this particular case (albeit not sufficient) due to scarce observational data. We present in this paper the model, its assumptions and limitations as well as application lifecycle with resulting ash distribution graphics. The computational experimental settings are described, in particular the use of Nimrod/G with respect to non-homogeneous parameter sweeps and its impact on execution time. We also analyze the implementation of a new parameter discard mechanism common to e-Science experiments where sequential generation of new parameter sets has to be complemented with an early verification in order to avoid allocation of CPU time to non-valid scenarios. Finally four sample 100K-scenario runs are analyzed for both traditional HPC clustering and Cloud computing resources in the Amazon EC2 Cloud.

In the process of learning a pattern I, the fuzzy ARTMAP algorithm templates (i.e., the weight vectors corresponding to nodes of its category representation layer) compete for the representation of the given pattern. This competition can inducematchtracking: a process that iterates a number of times over the template set searching for a templatew*of the correct class that best represents the pattern I. In this paper, we analyze the search for a winning template from the perspective of bi-criterion optimization and prove that it is actually a walk along the Pareto front of an appropriately defined co-objective optimization problem. This observation allows us to propose the basis for an implementation variant of Fuzzy ARTMAP that (a) produces exactly the same network as Fuzzy ARTMAP, (b) avoids matchtracking by explicitly keeping track of a subset of the Pareto front, (c) finds the correct template to represent an input pattern through a single pass over the template set and (d) eliminates the need for the Fuzzy ARTMAP parameter ε.

In this paper, we present a version of the probabilistic neural network (PNN) that is capable of operating on a distributed database that is horizontally partitioned. It does so in a way that is privacy-preserving: that is, a test point can be evaluated by the algorithm without any party knowing the data owned by the other parties. We present an analysis of this algorithm from the standpoints of security and computational performance. Finally, we provide performance results of an implementation of this privacy preserving, distributed PNN algorithm.

Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that it takes fuzzy ARTMAP to converge to a solution increases rapidly as the number of patterns used for training increases. In this paper, we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up fuzzy ARTMAP's training process. In particular, we first parallelized fuzzy ARTMAP, without the match-tracking mechanism, and then we parallelized fuzzy ARTMAP with the match-tracking mechanism. Results run on a Beowulf cluster with a well known large database (Forrest Covertype database from the UCI repository) show linear speedup with respect to the number of processors used in the pipeline.

One of the properties of FAM, which is a mixed blessing, is its capacity to produce new neurons (templates) on demand to represent classification categories. This property allows FAM to automatically adapt to the database without having to arbitrarily specify network structure, but it also has the undesirable side effect that on large databases it can produce a large network size that can dramatically slow down the algorithms' training time. To address this problem, we propose the use of the Hilbert space-filling curve. Our results indicate that the Hilbert space-filling curve can reduce the training time of FAM by partitioning the learning set without a significant effect on the classification performance or network size. Given that there is full data partitioning with the HSFC, we implement and test a parallel implementation on a Beowulf cluster of workstations that further speeds up the training and classification time on large databases.

This paper describes a multi-loop, modular navigation architecture for mobile robots whose structure allows the execution of most types of navigation tasks in a highly robust manner. The modularity allows the clear separation of functions according to their complexity and priority. We propose a clear separation between local and global navigation in such a way that both can run independently, but an adequate alternation/competition between them allows accomplishment of trajectory execution including avoidance of unknown obstacles. The multi-loop nature of the architecture ensures adequate stability at different levels yielding safe navigation and accomplishment of higher level tasks. Examples may range from goal reaching in some point of the environment to a 3D-environment mapping application, as this is the case in this work. 1. Introduction Navigation is a major issue when addressing mobile robotics because the concept is so wide that it includes all aspects of directing a robot's ...