Volumen 3 (2013): Edición 3 (July 2013)

We investigate structure of the Primary Language of the human brain as introduced by J. von Neumann in 1957. Two components have been investigated, the algorithm optimizing warfighting, Linguistic Geometry (LG), and the algorithm for inventing new algorithms, the Algorithm of Discovery. The latter is based on multiple thought experiments, which manifest themselves via mental visual streams (“mental movies”). There are Observation, Construction and Validation classes of streams. Several visual streams can run concurrently and exchange information between each other. The streams may initiate additional thought experiments, program them, and execute them in due course. The visual streams are focused employing the algorithm of “a child playing a construction set” that includes a visual model, a construction set, and the Ghost. Mosaic reasoning introduced in this paper is one of the major means to focusing visual streams in a desired direction. It uses analogy with an assembly of a picture of various colorful tiles, components of a construction set. In investigating role of mosaic reasoning in the Algorithm of Discovery, in this paper, I replay a series of four thought experiments related to the discovery of the structure of the molecule of DNA. Only the fourth experiment was successful. This series of experiments reveals how a sequence of failures eventually leads the Algorithm to a discovery. This series permits to expose the key components of the mosaic reasoning, tiles and aggregates, local and global matching rules, and unstructured environment. In particular, it reveals the aggregates and the rules that played critical role in the discovery of the structure of DNA. They include the generator and the plug-in aggregates, the transformation and complementarity matching rules, and the type of unstructured environment. For the first time, the Algorithm of Discovery has been applied to replaying discoveries not related to LG and even to mathematics

In this paper, the fixed final time adaptive optimal regulation of discrete-time linear systems with unknown system dynamics is addressed. First, by transforming the linear systems into the input/output form, the adaptive optimal control design depends only on the measured outputs and past inputs instead of state measurements. Next, due to the time-varying nature of finite-horizon, a novel online adaptive estimator is proposed by utilizing an online approximator to relax the requirement on the system dynamics. An additional error term corresponding to the terminal constraint is defined and minimized overtime. No policy/value iteration is performed by the novel parameter update law which is updated once a sampling interval. The proposed control design yields an online and forward-in-time solution which enjoys great practical advantages. Stability of the system is demonstrated by Lyapunov analysis while simulation results verify the effectiveness of the propose approach

This study aims to explore the possibility of improving human-robot interaction (HRI) by exploiting natural language resources and using natural language processing (NLP) methods. The theoretical basis of the study rests on the claim that effective and efficient human robot interaction requires linguistic and ontological agreement. A further claim is that the required ontology is implicitly present in the lexical and grammatical structure of natural language. The paper offers some NLP techniques to uncover (fragments of) the ontology hidden in natural language and to generate semantic representations of natural language sentences using that ontology. The paper also presents the implementation details of an NLP module capable of parsing English and Turkish along with an overview of the architecture of a robotic interface that makes use of this module for expressing the spatial motions of objects observed by a robot

This paper describes a computationally inexpensive approach to learning and identification of maneuverable terrain to aid autonomous navigation. We adopt a monocular vision based framework, using a single consumer grade camera as the primary sensor, and model the terrain as a Mixture of Gaussians. Self-supervised learning is used to identify navigable terrain in the perception space. Training data is obtained using pre-filtered pixels, which correspond to near-range traversable terrain. The scheme allows for on-line, and in-motion update of the terrain model. The pipeline architecture used in the proposed algorithm is made amenable to real-time implementation by restricting computations to bit-shifts and accumulate operations. Color based clustering using dominant terrain texture is then performed in perception sub-space. Model initialization and update follows at the coarse scale of an octave image pyramid, and is back projected onto the original fine scale. We present results of terrain learning, tested in heterogeneous environments, including urban road, suburban parks, and indoors. Our scheme provides orders of magnitude improvement in time complexity, when compared to existing approaches reported in literature

Population-Based Incremental Learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. PBIL has recently received increasing attention in various engineering fields due to its effectiveness, easy implementation and robustness. Despite these strengths, it was reported in the last few years that PBIL suffers from issues of loss of diversity in the population. To deal with this shortcoming, this paper uses parallel PBIL based on multi-population. In parallel PBIL, two populations are used where both probability vectors (PVs) are initialized to 0.5. It is believed that by introducing two populations, the diversity in the population can be increased and better results can be obtained. The approach is applied to power system controller design. Simulations results show that the parallel PBIL approach performs better than the standard PBIL and is as effective as another diversity increasing PBIL called adaptive PBIL