Massively parallel reasoning in transitive relationship hierarchies

This research focuses on building a parallel knowledge representation and reasoning system for the purpose of making progress in realizing human-like intelligence. To achieve human-like intelligence, it is necessary to model human reasoning processes by programs. Knowledge in the real world is huge in size, complex in structure, and is also constantly changing even in limited domains. Unfortunately, reasoning algorithms are very often intractable, which means that they are too slow for any practical applications. One technique to deal with this problem is to design special-purpose reasoners. Many past Al systems have worked rather nicely for limited problem sizes, but attempts to extend them to realistic subsets of world knowledge have led to difficulties. Even special purpose reasoners are not immune to this impasse. In this work, to overcome this problem, we are combining special purpose reasoners with massive We have developed and implemented a massively parallel transitive closure reasoner, called Hydra, that can dynamically assimilate any transitive, binary relation and efficiently answer queries using the transitive closure of all those relations. Within certain limitations, we achieve constant-time responses for transitive closure queries. Hydra can dynamically insert new concepts or new links into a. knowledge base for realistic problem sizes. To get near human-like reasoning capabilities requires the possibility of dynamic updates of the transitive relation hierarchies. Our incremental, massively parallel, update algorithms can achieve almost constant time updates of large knowledge bases. Hydra expands the boundaries of Knowledge Representation and Reasoning in a number of different directions: (1) Hydra improves the representational power of current systems. We have developed a set-based representation for class hierarchies that makes it easy to represent class hierarchies on arrays of processors. Furthermore, we have developed and implemented two methods for mapping this set-based representation onto the processor space of a Connection Machine. These two representations, the Grid Representation and the Double Strand Representation successively improve transitive closure reasoning in terms of speed and processor utilization. (2) Hydra allows fast rerieval and dynamic update of a large knowledge base. New fast update algorithms are formulated to dynamically insert new concepts or new relations into a knowledge base of thousands of nodes. (3) Hydra provides reasoning based on mixed hierarchical representations. We have designed representational tools and massively parallel reasoning algorithms to model reasoning in combined IS-A, Part-of, and Contained-in hierarchies. (4) Hydra\u27s reasoning facilities have been successfully applied to the Medical Entities Dictionary, a large medical vocabulary of Columbia Presbyterian Medical Center. As a result of (1) - (3), Hydra is more general than many current special-purpose reasoners, faster than currently existing general-purpose reasoners, and its knowledge base can be updated dynamically

GO-WORDS: An Entropic Approach to Semantic Decomposition of Gene Ontology Terms

The Gene Ontology (GO) has a large and growing number of terms that constitute its vocabulary. An entropy-based approach is presented to automate the characterization of the compositional semantics of GO terms. The motivation is to extend the machine-readability of GO and to offer insights for the continued maintenance and growth of GO. A proto-type implementation illustrates the benefits of the approach

Motion Tracking for Smart Home Care

Computational Infrastructure and Informatics Poster SessionHuman body motion capture has a wide range of applications and is being extensively investigated. Areas of application include virtual and augmented reality, biomechanics, sign language translation, gait analysis and graphics in movies and video games. The goal of this work is to develop an electronic device to translate arms motion and hand gestures into computer commands for smart home applications. This device is expected to improve the communication and lifestyle of elderly and disable people. We envision a device that is wearable, seamless and easy to use. Current state-of-the-art body motion capture employs high-speed and high-resolution cameras. Although this method is accurate and useful in laboratory settings, it requires the user to be inside the field of view of the cameras, a condition that is not always feasible during everyday activities. Instead, our approach relies on small sensors nodes that are worn on the wrists and around the waist. Inertial sensors such as accelerometers and gyroscopes have been employed before to develop wearable motion-tracking sensors due to their small size. However, they suffer from drift which causes the position estimations to have large errors. Ultrasonic sensors have also been employed to track motion. Although more accurate, ultrasonic sensors are affected by intermittent signal blockage produced by the body. Our approach is to combine these two sensing modalities in a way that the position estimation error is reduced. To that end, the outputs of the inertial and ultrasonic sensors are fused using a Kalman filter. The sensor nodes implement a multilateration algorithm that calculates the position of body-mounted sensors by measuring the time of travel of ultrasound bursts traveling between the sensor nodes. An electronic board for the sensor nodes have been designed, fabricated and programmed. The board measures 3.2 cm x 4.8 cm and includes a low-power microcontroller, a radio unit, a three-axis accelerometer, a two-axis gyroscope, an ultrasonic transmitter and an ultrasonic receiver. Our ongoing activities include the development of a 3D virtual simulation of a smart home. In the virtual smart home, various electronic devices such as computers, cell phones and household appliances like microwaves and televisions are networked for ubiquitous services. The wearable sensors capture the limb movements and relay this data to a central controller where it is interpreted to adjust the home environment. The sensors can also be used for emergency care by detecting any abnormal movements. Our approach will significantly improve current motion capture systems that are too cumbersome to wear or require the subject to be confined to a controlled environment or within the view range of the camera. Besides their use in smart home scenarios, the proposed wearable motion-tracking sensors can be used in biomechanic studies, virtual reality and interactive games