Reggia, James A. | Search NASA

NASA NTRS1994

A novel computational method to simulate non-enzymatic self-replication

Non-enzymatic, template-directed synthesis of oligonucleotides has been extensively studied in the laboratory as a model to understand the kind of chemical processes that might have contributed to the origin of life on Earth. Several oligonucleotides have been shown to catalyze the synthesis of their complements from activated mononucleotides; however, a restricted number of them have been found to self-replicate. Recently we developed an efficient modified cellular automata method that supports the study of self-replicating oligonucleotides. With this method the oligonucleotide molecules are represented as active cells imbedded in a two-dimensional array of inactive cells symbolizing the environment. Random movements and probability-governed chemical reactions occurring in a cellular space can effectively simulate the experimental behavior observed in self-directed replication of oligonucleotides.

Navarro-Gonzalez, Rafael↗

NASA NTRS1993

Simple systems that exhibit self-directed replication

Biological experience and intuition suggest that self-replication is an inherently complex phenomenon, and early cellular automata models support that conception. More recently, simpler computational models of self-directed replication called sheathed loops have been developed. It is shown here that 'unsheathing' these structures and altering certain assumptions about the symmetry of their components leads to a family of nontrivial self-replicating structures some substantially smaller and simpler than those previously reported. The dependence of replication time and transition function complexity on initial structure size, cell state symmetry, and neighborhood are examined. These results support the view that self-replication is not an inherently complex phenomenon but rather an emergent property arising from local interactions in systems that can be much simpler than is generally believed.

Reggia, James A.↗

NASA NTRS1990

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.

Dautrechy, C. Lynne↗

NASA NTRS1989

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.

Dautrechy, C. Lynne↗

NASA NTRS1989

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.

Bourret, P.↗

NASA NTRS1989

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.

Whitfield, Kevin C.↗

NASA NTRS1989

A connectionist model for diagnostic problem solving

A competition-based connectionist model for solving diagnostic problems is described. The problems considered are computationally difficult in that (1) multiple disorders may occur simultaneously and (2) a global optimum in the space exponential to the total number of possible disorders is sought as a solution. The diagnostic problem is treated as a nonlinear optimization problem, and global optimization criteria are decomposed into local criteria governing node activation updating in the connectionist model. Nodes representing disorders compete with each other to account for each individual manifestation, yet complement each other to account for all manifestations through parallel node interactions. When equilibrium is reached, the network settles into a locally optimal state. Three randomly generated examples of diagnostic problems, each of which has 1024 cases, were tested, and the decomposition plus competition plus resettling approach yielded very high accuracy.

Peng, Yun↗

NASA NTRS1989

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.

D'Autrechy, C. Lynne↗

NASA NTRS1989

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.

Whitfield, Kevin C.↗