A method for interactive satellite failure diagnosis: Towards a connectionist solution

Various kinds of processes which allow one to make a diagnosis are analyzed. The analyses then focuses on one of these processes used for satellite failure diagnosis. This process consists of sending the satellite instructions about system status alterations: to mask the effects of one possible component failure or to look for additional abnormal measures. A formal model of this process is given. This model is an extension of a previously defined connectionist model which allows computation of ratios between the likelihoods of observed manifestations according to various diagnostic hypotheses. The expected mean value of these likelihood measures for each possible status of the satellite can be computed in a similar way. Therefore, it is possible to select the most appropriate status according to three different purposes: to confirm an hypothesis, to eliminate an hypothesis, or to choose between two hypotheses. Finally, a first connectionist schema of computation of these expected mean values is given

Parallel plan execution with self-processing networks

A critical issue for space operations is how to develop and apply advanced automation techniques to reduce the cost and complexity of working in space. In this context, it is important to examine how recent advances in self-processing networks can be applied for planning and scheduling tasks. For this reason, the feasibility of applying self-processing network models to a variety of planning and control problems relevant to spacecraft activities is being explored. Goals are to demonstrate that self-processing methods are applicable to these problems, and that MIRRORS/II, a general purpose software environment for implementing self-processing models, is sufficiently robust to support development of a wide range of application prototypes. Using MIRRORS/II and marker passing modelling techniques, a model of the execution of a Spaceworld plan was implemented. This is a simplified model of the Voyager spacecraft which photographed Jupiter, Saturn, and their satellites. It is shown that plan execution, a task usually solved using traditional artificial intelligence (AI) techniques, can be accomplished using a self-processing network. The fact that self-processing networks were applied to other space-related tasks, in addition to the one discussed here, demonstrates the general applicability of this approach to planning and control problems relevant to spacecraft activities. It is also demonstrated that MIRRORS/II is a powerful environment for the development and evaluation of self-processing systems

Autoplan: A self-processing network model for an extended blocks world planning environment

Self-processing network models (neural/connectionist models, marker passing/message passing networks, etc.) are currently undergoing intense investigation for a variety of information processing applications. These models are potentially very powerful in that they support a large amount of explicit parallel processing, and they cleanly integrate high level and low level information processing. However they are currently limited by a lack of understanding of how to apply them effectively in many application areas. The formulation of self-processing network methods for dynamic, reactive planning is studied. The long-term goal is to formulate robust, computationally effective information processing methods for the distributed control of semiautonomous exploration systems, e.g., the Mars Rover. The current research effort is focusing on hierarchical plan generation, execution and revision through local operations in an extended blocks world environment. This scenario involves many challenging features that would be encountered in a real planning and control environment: multiple simultaneous goals, parallel as well as sequential action execution, action sequencing determined not only by goals and their interactions but also by limited resources (e.g., three tasks, two acting agents), need to interpret unanticipated events and react appropriately through replanning, etc

A connectionist model for dynamic control

The application of a connectionist modeling method known as competition-based spreading activation to a camera tracking task is described. The potential is explored for automation of control and planning applications using connectionist technology. The emphasis is on applications suitable for use in the NASA Space Station and in related space activities. The results are quite general and could be applicable to control systems in general

Measuring Organization and Asymmetry in Bihemispheric Topographic Maps

We address the problem of measuring the degree of hemispheric organization and asymmetry of organization in a computational model of a bihemispheric cerebral cortex. A theoretical framework for such measures is developed and used to produce algorithms for measuring the degree of organization, symmetry, and lateralization in topographic map formation. The performance of the resulting measures is tested for several topographic maps obtained by self--organization of an initially random network, and the results are compared with subjective assessments made by humans. It is found that the closest agreement with the human assessments is obtained by using organization measures based on sigmoid--type error averaging. Measures are developed which correct for large constant displacements as well as curving of the hemispheric topographic maps. (Also cross-referenced as UMIACS-TR-96-51

A SIMULATION ENVIRONMENT FOR EVOLVING MULTIAGENT COMMUNICATION

A simulation environment has been created to support study of emergent communication. Multiple agents exist in a two-dimensional world where they must find food and avoid predators. While non-communicating agents may survive, the world is configured so that survival and fitness can be enhanced through the use of inter-agent communication. The goal with this version of the simulator is to determine conditions under which simple communication (signaling) emerges and persists during an evolutionary process. (Also cross-referenced as UMIACS-TR-2000-64

The Maryland Virtual Demonstrator Environment for Robot Imitation Learning

Robot imitation learning, where a robot autonomously generates actions required to accomplish a task demonstrated by a human, has emerged as a potential replacement for a more conventional hand-coded approach to programming robots. Many past studies in imitation learning have human demonstrators perform tasks in the real world. However, this approach is generally expensive and requires high-quality image processing and complex human motion understanding. To address this issue, we developed a simulated environment for imitation learning, where visual properties of objects are simplified to lower the barriers of image processing. The user is provided with a graphical user interface (GUI) to demonstrate tasks by manipulating objects in the environment, from which a simulated robot in the same environment can learn. We hypothesize that in many situations, imitation learning can be significantly simplified while being more effective when based solely on objects being manipulated rather than the demonstrator's body and motions. For this reason, the demonstrator in the environment is not embodied, and a demonstration as seen by the robot consists of sequences of object movements. A programming interface in Matlab is provided for researchers and developers to write code that controls the robot's behaviors. An XML interface is also provided to generate objects that form task-specific scenarios. This report describes the features and usages of the software

A Behavior-to-Brain Map

Development and study of large-scale computational models of the human brain, and their use to simulate cognitive functions, is becoming increasingly feasible. However, construction of integrated models that span multiple cognitive systems (language, memory, reasoning, learning, sensorimotor control, executive functions, etc.) is currently inhibited by the absence of any systematic catalog of experimentally documented associations between specific behavioral functions and specific brain regions. In this report we provide a prototype for such a mapping in the form of a semantic network. While preliminary and not comprehensive, the results presented here support the idea that an online mapping between cognitive function and cortical/subcortical structures can be developed as a useful reference source

SMILE: Simulator for Maryland Imitation Learning Environment

As robot imitation learning is beginning to replace conventional hand-coded approaches in programming robot behaviors, much work is focusing on learning from the actions of demonstrators. We hypothesize that in many situations, procedural tasks can be learned more effectively by observing object behaviors while completely ignoring the demonstrator's motions. To support studying this hypothesis and robot imitation learning in general, we built a software system named SMILE that is a simulated 3D environment. In this virtual environment, both a simulated robot and a user-controlled demonstrator can manipulate various objects on a tabletop. The demonstrator is not embodied in SMILE, and therefore a recorded demonstration appears as if the objects move on their own. In addition to recording demonstrations, SMILE also allows programing the simulated robot via Matlab scripts, as well as creating highly customizable objects for task scenarios via XML. This report describes the features and usages of SMILE