Planning for execution monitoring on a planetary rover

A planetary rover will be traversing largely unknown and often unknowable terrain. In addition to geometric obstacles such as cliffs, rocks, and holes, it may also have to deal with non-geometric hazards such as soft soil and surface breakthroughs which often cannot be detected until rover is in imminent danger. Therefore, the rover must monitor its progress throughout a traverse, making sure to stay on course and to detect and act on any previously unseen hazards. Its onboard planning system must decide what sensors to monitor, what landmarks to take position readings from, and what actions to take if something should go wrong. The planning systems being developed for the Pathfinder Planetary Rover to perform these execution monitoring tasks are discussed. This system includes a network of planners to perform path planning, expectation generation, path analysis, sensor and reaction selection, and resource allocation

Complex Event Recognition Architecture

Complex Event Recognition Architecture (CERA) is the name of a computational architecture, and software that implements the architecture, for recognizing complex event patterns that may be spread across multiple streams of input data. One of the main components of CERA is an intuitive event pattern language that simplifies what would otherwise be the complex, difficult tasks of creating logical descriptions of combinations of temporal events and defining rules for combining information from different sources over time. In this language, recognition patterns are defined in simple, declarative statements that combine point events from given input streams with those from other streams, using conjunction, disjunction, and negation. Patterns can be built on one another recursively to describe very rich, temporally extended combinations of events. Thereafter, a run-time matching algorithm in CERA efficiently matches these patterns against input data and signals when patterns are recognized. CERA can be used to monitor complex systems and to signal operators or initiate corrective actions when anomalous conditions are recognized. CERA can be run as a stand-alone monitoring system, or it can be integrated into a larger system to automatically trigger responses to changing environments or problematic situations

OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression

<p>Abstract</p> <p>Background</p> <p>Information extraction (IE) efforts are widely acknowledged to be important in harnessing the rapid advance of biomedical knowledge, particularly in areas where important factual information is published in a diverse literature. Here we report on the design, implementation and several evaluations of OpenDMAP, an ontology-driven, integrated concept analysis system. It significantly advances the state of the art in information extraction by leveraging knowledge in ontological resources, integrating diverse text processing applications, and using an expanded pattern language that allows the mixing of syntactic and semantic elements and variable ordering.</p> <p>Results</p> <p>OpenDMAP information extraction systems were produced for extracting protein transport assertions (transport), protein-protein interaction assertions (interaction) and assertions that a gene is expressed in a cell type (expression). Evaluations were performed on each system, resulting in F-scores ranging from .26 – .72 (precision .39 – .85, recall .16 – .85). Additionally, each of these systems was run over all abstracts in MEDLINE, producing a total of 72,460 transport instances, 265,795 interaction instances and 176,153 expression instances. </p> <p>Conclusion</p> <p>OpenDMAP advances the performance standards for extracting protein-protein interaction predications from the full texts of biomedical research articles. Furthermore, this level of performance appears to generalize to other information extraction tasks, including extracting information about predicates of more than two arguments. The output of the information extraction system is always constructed from elements of an ontology, ensuring that the knowledge representation is grounded with respect to a carefully constructed model of reality. The results of these efforts can be used to increase the efficiency of manual curation efforts and to provide additional features in systems that integrate multiple sources for information extraction. The open source OpenDMAP code library is freely available at <url>http://bionlp.sourceforge.net/</url></p

Building Symbolic Primitives with Continuous Control Routines

This paper is about the interface between continuous and symbolic robot control. We advocate describing continuous actions and their related sensing strategies as situation specific activities, which can be manipulated by a symbolic reactive planner. The approach addresses the issues involved in turning symbolic actions into continuous activities, and using task specific sensing routines to support those activities. Situation specific activities help preserve the convenient fiction of &quot;primitive actions&quot; for use in planning without requiring that they all be programmed into the control system in advance. We demonstrate the utility of this architecture with an object tracking example. A control system is presented that can be reconfigured by a the rap reactive executor to achieve different tasks. We show how this system allows us to build interchangeable tracking activities that use different sensing /action feedback loops in different situations. First Internation..

Pliocene non-marine mollusks from Contra Costa County, California

Volume: 34Start Page: 511End Page: 52

An architecture for a synthetic vacuum cleaner

This paper discusses three increasingly complex definitions of the vacuum cleaning task. Each definition requires different capabilities in the vacuuming agent and hence different internal software architectures. However, the definitions suggested form a progression from reactive, to synthetic, to intelligent, and the lessons learned looking at one problem supply important insights for tackling the next. The paper then describes the Animate Agent project which defines an architecture for robot control that attempts to embody both the reactive methods of control required in simple vacuum cleaners and the symbolic methods of situation classification and plan choice required by synthetic vacuum cleaners.

Combined Execution and Monitoring for Control of Autonomous Agents

Robots and software agents that have to carry out tasks in the real world often have to initiate actions that involve continuous change. But activity in the real world also involves interacting with continous external processes¯those initiated by the agent and those initiated by other factors in the world. In this paper, we present an architecture called rama, for Routine Activity Management and Analysis, which combines plan execution and process monitoring. rama uses a single process based representation to describe plans, external processes, and the way they must be coordinated. As it goes about performing tasks, rama uses a reactive executor to select actions and make predictions based on stored plans and process models. We illustrate the above behaviors with examples taken from laundry world, a simulation in which rama controls a synthetic agent that does laundry. i Contents 1 Introduction 1 2 Knowing What To Do and What To Expect 1 3 Overview of RAMA&apos;s Operation 2 4 Represent..

Task Networks for Controlling Continuous Processes

This paper describes an extension to the rap system task-net semantics and representation language to enable the effective control of continuous processes. The representation addresses the problems of synchronizing plan expansion with events in the world, coping with multiple, non-deterministic task outcomes, and the description of a simple form of clean-up task. It is also pointed out that success and failure need no special place in a task network representation. Success and failure are really messages about the execution system&apos;s knowledge and do not explicitly define that system&apos;s flow of control. To Appear in the Second International Conference on AI Planning Systems, June 1994. 1 Introduction Recently, AI researchers have proposed several different mechanisms for programming robots reactively. These include collections of behaviors [2], schemas [1], routines [9], and reflexes [15]. Many details differ between these proposals, particularly in the area of philosop..