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A truly intelligent system that interacts with the physical world must be endowed with the ability the compute with shapes: despite this, spatial reasoning is rarely regarded as part of mainstream artificial intelligence. Here, researchers argue that the study of intelligent spatial algorithms is a worthwhile activity, and give opinions and suggestions for the way forward.
Echogram recordings are used to estimate the acoustic quality of a room. Using two methods based on geometric acoustics, echograms are computed for rooms of irregular shape. The image method and the ray method have been applied to a heptahedral room as well as a conference hall. The results obtained, which have been verified experimentally, permit an estimate of the intelligibility coefficient.
Artificial intelligence - system for instructing computer and programming language for manipulating symbolic expressions
NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.
In this paper, we will present a look at the current state of the art in human-computer interface technologies, including intelligent interactive agents, natural speech interaction and gestural based interfaces. We describe our use of these technologies to implement a cost effective, immersive experience on a public region in Second Life. We provision our Artificial Agents as a German Shepherd Dog avatar with an external rules engine controlling the behavior and movement. To interact with the avatar, we implemented a natural language and gesture system allowing the human avatars to use speech and physical gestures rather than interacting via a keyboard and mouse. The result is a system that allows multiple humans to interact naturally with AI avatars by playing games such as fetch with a flying disk and even practicing obedience exercises using voice and gesture, a natural seeming day in the park.
In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial instruction presented here has two parts. First, a computational model of an intelligent agent, the problem space computational model, indicates the types of knowledge that determine an agent's performance, and thus, that should be acquirable via instruction. Second, a learning technique, called situated explanation specifies how the agent learns general knowledge from instruction. The theory is embodied by an implemented agent, Instructo-Soar, built within the Soar architecture. Instructo-Soar is able to learn hierarchies of completely new tasks, to extend task knowledge to apply in new situations, and in fact to acquire every type of knowledge it uses during task performance - control knowledge, knowledge of operators' effects, state inferences, etc. - from interactive natural language instructions. This variety of learning occurs by applying the situated explanation technique to a variety of instructional interactions involving a variety of types of instructions (commands, statements, conditionals, etc.). By taking seriously the requirements of flexible tutorial instruction, Instructo-Soar demonstrates a breadth of interaction and learning capabilities that goes beyond previous instructable systems, such as learning apprentice systems. Instructo-Soar's techniques could form the basis for future 'instructable technologies' that come equipped with basic capabilities, and can be taught by novice users to perform any number of desired tasks.
One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.
Computing architectures are being assembled that extend concurrent engineering practices by providing more efficient execution and collaboration on distributed, heterogeneous computing networks. Built on the successes of initial architectures, requirements for a next-generation design computing infrastructure can be developed. These requirements concentrate on those needed by a designer in decision-making processes from product conception to recycling and can be categorized in two areas: design process and design information management. A designer both designs and executes design processes throughout design time to achieve better product and process capabilities while expanding fewer resources. In order to accomplish this, information, or more appropriately design knowledge, needs to be adequately managed during product and process decomposition as well as recomposition. A foundation has been laid that captures these requirements in a design architecture called DREAMS (Developing Robust Engineering Analysis Models and Specifications). In addition, a computing infrastructure, called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment), is being developed that satisfies design requirements defined in DREAMS and incorporates enabling computational technologies.
A wide range of computational models and analyses have been applied to spaceflight risk assessment and countermeasure development. The benefits of using computational modeling to enhance Human Research Program (HRP) goals include the ability to mathematically represent physiological systems, integrate multiple, discrete experimental measures, span multiple temporal and spatial scales, determine important factors within the system and provide estimates of unmeasurable quantities. In the area of application, computational models provide a means of developing simulations to test hypotheses, determining key factors of the system to aid experimental design and bridging gaps in sparse data by mathematically simulating large populations. Specifically, computational models and their supporting analysis tools have the proven potential to integrate analyses of risk factors to enhance mission planning and preparation capabilities and to inform spacecraft design and countermeasure development. Appropriately applied, computational models may allow intelligent, unbiased physiological parameter assessment to enable hypothesis testing and model based design of experiments. HRP recently formed the Computational Modeling Project (CMP), managed out of Glenn Research Center, as a cross-cutting activity aimed at leveraging the growing power and acceptance of computational modeling in informing clinical, physiological, and biological studies. This presentation will provide an overview of the challenges and opportunities in implementing various forms of computational models in support of the HRPs path to risk reduction.
Techniques for integrating microprocessors, array processors, and other intelligent devices in control systems are reviewed, with an emphasis on the (re)arrangement of components to form distributed or parallel processing systems. Consideration is given to the selection of the host microprocessor, increasing the power and/or memory capacity of the host, multitasking software for the host, array processors to reduce computation time, the allocation of real-time and non-real-time events to different computer subsystems, intelligent devices to share the computational burden for real-time events, and intelligent interfaces to increase communication speeds. The case of a helicopter vibration-suppression and stabilization controller is analyzed as an example, and significant improvements in computation and throughput rates are demonstrated.
A system capable of pointing a balloon-borne telescope at selected celestial objects to an accuracy of approximately 10 arc minutes for an extended period (weeks to months) without reliance on telemetry is described. A unique combination of a sun/star tracker, an on-board computer, and a gyrocompass is utilized for navigation, source acquisition and tracking, and data compression and recording. The possibilities for intelligent activities by the computer are also discussed.
Computer-Aided Instruction (CAI) is a mature technology used to teach students in a wide variety of domains. The introduction of Artificial Intelligence (AI) technology of the field of CAI has prompted research and development efforts in an area known as Intelligent Computer-Aided Instruction (ICAI). In some cases, ICAI has been touted as a revolutionary alternative to traditional CAI. With the advent of powerful, inexpensive school computers, ICAI is emerging as a potential rival to CAI. In contrast to this, one may conceive of Computer-Based Training (CBT) systems as lying along a continuum which runs from CAI to ICAI. Although the key difference between the two is intelligence, there is not commonly accepted definition of what constitutes an intelligent instructional system.
Topics explored in the development of integrated programmable automation systems include: numerically controlled and computer controlled machining; machine intelligence and the emulation of human-like capabilities; large scale semiconductor integration technology applications; and sensor technology for asynchronous local computation without burdening the executive minicomputer which controls the whole system. The role and development of training aids, and the potential application of these aids to augmented teleoperator systems are discussed.
Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design.
Recommendations are made for upgrading two teleoperator/robotics test and simulation systems based upon a review of latest technology advances in the involved disciplines. A second generation Free Flying Mobility Unit is recommended which adds a sixth degree of freedom and incorporates other improvements which greatly expand the center's capability to perform evaluation tests and demonstrations of advanced systems concepts for rendezvous and docking in support of the Teleoperator Maneuvering System (TMS) Program. The Orbital Servicer System provides the capability for testing and demonstrating concepts for on orbit servicing of compatibly designed satellites/payloads. The TMS is to be the transporting vehicle for the servicer. The manipulator arm of the Orbital service System is presently computer controlled in the trajectory portion of the module transfer operation. The ultimate objective is to fully automte its operation requiring additional capabilities in sensors, artificial intelligence, image analysis, communications, computer programming, pattern recognition, kinematics, and manipulator design. It is recommended that the Electronics and Control Laboratory move to acquire the basic competencies in robotics necessary to achieve full automation.
Computer program to decide structure for aliphatic ketone from low resolution mass spectrum using DENDRAL algorithm
The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype.
Version 3.1 of Symbolic Constraint Maintenance Grid (SCMG) is a software system that provides a general conceptual framework for utilizing pre-existing programming techniques to perform symbolic transformations of data. SCMG also provides a language (and an associated communication method and protocol) for representing constraints on the original non-symbolic data. SCMG provides a facility for exchanging information between numeric and symbolic components without knowing the details of the components themselves. In essence, it integrates symbolic software tools (for diagnosis, prognosis, and planning) with non-artificial-intelligence software. SCMG executes a process of symbolic summarization and monitoring of continuous time series data that are being abstractly represented as symbolic templates of information exchange. This summarization process enables such symbolic- reasoning computing systems as artificial- intelligence planning systems to evaluate the significance and effects of channels of data more efficiently than would otherwise be possible. As a result of the increased efficiency in representation, reasoning software can monitor more channels and is thus able to perform monitoring and control functions more effectively.