Several problems need to be addressed when integrating reactive and deliberative layers in a hybrid architecture, most importantly the different time scales of operation and (possibly) different data representations of the individual layers. This paper proposes an interface component called GLUE that is designed to mediate between a schema-based reactive and higher-level deliberative layer. We demonstrate the viability of the conceptual architectural design by defining and implementing a schema-based reactive architecture for a ball following task on an autonomous agent, which is then extended by the GLUE component to interface with a simple deliberative layer.
Abstract-- Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot. I. INTRODUCTION One of the problems an autonomous robot must be able to solve is to retrieve basic information about its own topological structure relying on minimal a priori information.
The multifunctional autoprocessing repeats-in-toxin (MARTX) toxins are a family of large toxins that are extensively distributed in bacterial pathogens. MARTX toxins are autocatalytically cleaved to multiple effector domains, which are released into host cells to modulate the host signaling pathways. The Rho guanosine triphosphatase (GTPase) inactivation domain (RID), a conserved effector domain of MARTX toxins, is implicated in cell rounding by disrupting the host actin cytoskeleton.
This work presents an alternate method for representing a planner's domain knowledge and generating operator instantiations, which we call effecforbased operator construction. The objective of this method is to make planner knowledge acquisition easier and less time consuming in complex physical domains such as manufacturing planning by making it possible for users who are domain experts, but not necessarily programmers, to perform the maintenance task. Thus, enabling them to customize the planning tool to meet the changing needs of their specific environment. This work is motivated by the fact that the world is not static. Because it constantly changes, intelligent software systems must be updated constantly to deal with these changes.
I propose a mechanism to model aspects of Piagetian development, in infants. The mechanism combines a powerful empirical learning technique with an unusual facility for constructing novel elements of representation-elements designating states that are not, mere logical combin&ions of other represented states. I sketch how this mechanism might recapitulate the infant's gradual recognition that there exist physical objects that persist even when the infant does not perceive them. I also report results of a preliminary, partial implementati0n.l Piaget supplies elaborate observations of characteristic behaviors at each developmental stage as reflections of the infant's underlying representations of the world. But, Piaget stops short of explaining what mechanism underpins the development he describes; that is the goal of my present effort. I take Piagetian development as a working hypothesis; trying to implement it is a way to test and refine the hypothesis.