A neural network-based trajectory planner for redundant systems using direct inverse modeling

Redundant (i.e., under-determined) systems can not be trained effectively using direct inverse modeling with supervised learning, for reasons well out-lined by Michael Jordan at MIT. There is a loop-hole , however, in Jordan\u27s preconditions, which seems to allow just such an architecture. A robot path planner implementing a cerebellar inspired habituation paradigm with such an architecture will be introduced. The system, called ARTFORMS, for Adaptive Redundant Trajectory Formation System uses on-line training of multiple CMACS. CMACs are locally generalizing networks, and have an a priori deterministic geometric input space mapping. These properties together with on-line learning and rapid convergence satisfy the loop-hole conditions. Issues of stability/plasticity, presentation order and generalization, computational complexity, and subsumptive fusion of multiple networks are discussed. Two implementations are described. The first is shown not to be goal directed enough for ultimate success. The second, which is highly successful, is made more goal directed by the addition of secondary training, which reduces the dimensionality of the problem by using a set of constraint equations. Running open loop with respect to posture (the system metric which reduces dimensionality) is seen to be the root cause of the first system\u27s failure, not the use of the direct inverse method. In fact, several nice properties of direct inverse modeling contribute to the system\u27s convergence speed, robustness and compliance. The central problem used to demonstrate this method is the control of trajectory formation for a planar kinematic chain with a variable number of joints. Finally, this method is extended to implement adaptive obstacle avoidance

First CLIPS Conference Proceedings, volume 2

The topics of volume 2 of First CLIPS Conference are associated with following applications: quality control; intelligent data bases and networks; Space Station Freedom; Space Shuttle and satellite; user interface; artificial neural systems and fuzzy logic; parallel and distributed processing; enchancements to CLIPS; aerospace; simulation and defense; advisory systems and tutors; and intelligent control

The Translocal Event and the Polyrhythmic Diagram

This thesis identifies and analyses the key creative protocols in translocal performance practice, and ends with suggestions for new forms of transversal live and mediated performance practice, informed by theory. It argues that ontologies of emergence in dynamic systems nourish contemporary practice in the digital arts. Feedback in self-organised, recursive systems and organisms elicit change, and change transforms. The arguments trace concepts from chaos and complexity theory to virtual multiplicity, relationality, intuition and individuation (in the work of Bergson, Deleuze, Guattari, Simondon, Massumi, and other process theorists). It then examines the intersection of methodologies in philosophy, science and art and the radical contingencies implicit in the technicity of real-time, collaborative composition. Simultaneous forces or tendencies such as perception/memory, content/ expression and instinct/intellect produce composites (experience, meaning, and intuition- respectively) that affect the sensation of interplay. The translocal event is itself a diagram - an interstice between the forces of the local and the global, between the tendencies of the individual and the collective. The translocal is a point of reference for exploring the distribution of affect, parameters of control and emergent aesthetics. Translocal interplay, enabled by digital technologies and network protocols, is ontogenetic and autopoietic; diagrammatic and synaesthetic; intuitive and transductive. KeyWorx is a software application developed for realtime, distributed, multimodal media processing. As a technological tool created by artists, KeyWorx supports this intuitive type of creative experience: a real-time, translocal “jamming” that transduces the lived experience of a “biogram,” a synaesthetic hinge-dimension. The emerging aesthetics are processual – intuitive, diagrammatic and transversal

Разрешавање идентитета и груписање дигиталних доказа о осумњиченима применом технологија препознавања лица и система софтверских интелигентих агената заснованог на неаксиоматском резоновању

The work of criminal police in modern society is characterized by the proliferation of data and information to be processed, greater demands for restrictions on personal data, increased public monitoring, and higher expectations in the efficiency of detecting perpetrators, but still lack resources, both human and material. One of the more complex tasks is to resolve the identity, the change of which seeks to cover up criminal activities, i.e., the perpetrator himself, who is on the run. In order to resolve the identity, it is necessary to group and present all available evidence related to specific persons. The thesis proposes a clustering approach by comparing pairs of face feature vectors extracted from images created in unconstrained conditions and based on reasoning using non-axiomatic logic and graphs. Face clusters will be the central points around which data from various police reports will be grouped. A system model has also been proposed in which software agents will play a significant role, primarily in connecting the distribution environment points formed in practice by police information systems. The clustering approach was experimentally tested with six different face image databases characterized by the fact that they were created in a way that simulates unconstrained conditions. The obtained results of the proposed solution are compared with other state-of-the-art methods. The results showed that the approach gives similar but mostly better results than the others. What gives a notable advantage over other methods is the possibility of using mechanisms from non-axiomatic logic such as revision and deduction, which can be used to acquire new knowledge based on information from different system nodes, or in the local knowledge base, respectively.Рад криминалистичке полиције у савременом друштву одликује пролиферација података и информација које треба обрађивати, већи захтеви за ограничењима у раду са личним подацима, појачани надзор пре свега јавности, већа очекивања у ефикасности откривања извршилаца кривичних дела, али и даље недостатак ресурса, како људских тако и материјалних. Један од сложенијих задатака јесте разрешавање идентитета чијом променом се настоје прикрити криминалне активности, односно сам извршилац који је у бекству. Да би се разрешио идентитет, потребно је груписати и презентовати све расположиве доказе везане за одређене особе. У дисертацији је предложен нови приступ кластеровању поређењем парова вектора одлика лица екстрахованих из слика насталих у неконтролисаним условима, а заснован на резоновању применом неаксиоматске логике и графова. Кластери слика лица представљају централне тачке око којих се групишу подаци из различитих полицијских извештаја. Такође је предложен модел система у коме ће значајну улогу имати софтверски агенти, пре свега у повезивању тачака дистрибуираног окружења које у пракси формирају полицијски информациони системи. Нови приступ кластеровању је експериментално испитан са шест различитих база података лица карактеристичних по томе што су креиране на начин којим се симулирају неконтролисани услови. Добијени резултати предложеног решења су упоређени са осталим врхунским методама. Резултати су показали да приступ даје приближне, али углавном боље резултате од осталих. Оно што даје посебну предност у односу на остале методе јесте могућност коришћења механизама из неаксиоматске логике попут ревизије и дедукције, помоћу којих се могу стицати нова знања на основу информација из различитих нодова система, или у локалној бази знања, респективно

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms