Engineering topics

Jonsson, Ari K.

Publications and source records attributed to Jonsson, Ari K..

12 records

NASA NTRS2005

Mixed-Initiative Planning in MAPGEN: Capabilities and Shortcomings

MAPGEN (Mixed-initiative Activity Plan GENerator) is a mixed-initiative system that employs automated constraint-based planning, scheduling, and temporal reasoning to assist the Mars Exploration Rover mission operations staff in generating the daily activity plans. This paper describes the mixed-initiative capabilities of MAPGEN, identifies shortcomings with the deployed system, and discusses ongoing work to address some of these shortcomings.

Bresina, John L.↗

NASA NTRS2004

Activity Planning for the Mars Exploration Rovers

Operating the Mars Exploration Rovers is a challenging, time-pressured task. Each day, the operations team must generate a new plan describing the rover activities for the next day. These plans must abide by resource limitations, safety rules, and temporal constraints. The objective is to achieve as much science as possible, choosing from a set of observation requests that oversubscribe rover resources. In order to accomplish this objective, given the short amount of planning time available, the MAPGEN (Mixed-initiative Activity Plan GENerator) system was made a mission-critical part of the ground operations system. MAPGEN is a mixed-initiative system that employs automated constraint-based planning, scheduling, and temporal reasoning to assist operations staff in generating the daily activity plans. This paper describes the adaptation of constraint-based planning and temporal reasoning to a mixed-initiative setting and the key technical solutions developed for the mission deployment of MAPGEN.

Bresina, John L.↗

NASA NTRS2004

Mixed-Initiative Constraint-Based Activity Planning for Mars Exploration Rovers

In January, 2004, two NASA rovers, named Spirit and Opportunity, successfully landed on Mars, starting an unprecedented exploration of the Martian surface. Power and thermal concerns constrained the duration of this mission, leading to an aggressive plan for commanding both rovers every day. As part of the process for generating these command loads, the MAPGEN tool provides engineers and scientists an intelligent activity planning tool that allows them to more effectively generate complex plans that maximize the science return each day. The key to'the effectiveness of the MAPGEN tool is an underlying artificial intelligence plan and constraint reasoning engine. In this paper we outline the design and functionality of the MAEPGEN tool and focus on some of the key capabilities it offers to the MER mission engineers.

Bresina, John↗

NASA NTRS2003

Scoping Planning Agents With Shared Models

In this paper we provide a formal framework to define the scope of planning agents based on a single declarative model. Having multiple agents sharing a single model provides numerous advantages that lead to reduced development costs and increase reliability of the system. We formally define planning in terms of extensions of an initial partial plan, and a set of flaws that make the plan unacceptable. A Flaw Filter (FF) allows us to identify those flaws relevant to an agent. Flaw filters motivate the Plan Identification Function (PIF), which specifies when an agent is is ready hand control to another agent for further work. PIFs define a set of plan extensions that can be generated from a model and a plan request. FFs and PIFs can be used to define the scope of agents without changing the model. We describe an implementation of PIFsand FFswithin the context of EUROPA, a constraint-based planning architecture, and show how it can be used to easily design many different agents.

Bedrax-Weiss, Tania↗

NASA NTRS2003

MAPGEN Planner: Mixed-Initiative Activity Planning for the Mars Exploration Rover Mission

This document describes the Mixed-initiative Activity Plan Generation system MAPGEN. The system is be- ing developed as one of the tools to be used during surface operations of NASA's Mars Exploration Rover mission (MER). However, the core technology is general and can be adapted to different missions and applications. The motivation for the system is to better support users that need to rapidly build activity plans that have to satisfy complex rules and fit within resource limits. The system therefore combines an existing tool for activity plan editing and resource modeling, with an advanced constraint-based reasoning and planning framework. The demonstration will show the key capabilities of the automated reasoning and planning component of the system, with emphasis on how these capabilities will be used during surface operations of the MER mission.

Ai-Chang, Mitch↗

NASA NTRS2003

Identifying Executable Plans

Generating plans for execution imposes a different set of requirements on the planning process than those imposed by planning alone. In highly unpredictable execution environments, a fully-grounded plan may become inconsistent frequently when the world fails to behave as expected. Intelligent execution permits making decisions when the most up-to-date information is available, ensuring fewer failures. Planning should acknowledge the capabilities of the execution system, both to ensure robust execution in the face of uncertainty, which also relieves the planner of the burden of making premature commitments. We present Plan Identification Functions (PIFs), which formalize what it means for a plan to be executable, md are used in conjunction with a complete model of system behavior to halt the planning process when an executable plan is found. We describe the implementation of plan identification functions for a temporal, constraint-based planner. This particular implementation allows the description of many different plan identification functions. characteristics crf the ~xec~~tieonfvii r~nm-enft,h e best plan to hand to the execution system will contain more or less commitment and information.

Bedrax-Weiss, Tania↗

NASA NTRS2001

The Logic of Reachability

In recent years, Graphplan style reachability analysis and mutual exclusion reasoning have been used in many high performance planning systems. While numerous refinements and extensions have been developed, the basic plan graph structure and reasoning mechanisms used in these systems are tied to the very simple STRIPS model of action. In 1999, Smith and Weld generalized the Graphplan methods for reachability and mutex reasoning to allow actions to have differing durations. However, the representation of actions still has some severe limitations that prevent the use of these techniques for many real-world planning systems. In this paper, we 1) separate the logic of reachability from the particular representation and inference methods used in Graphplan, and 2) extend the notions of reachability and mutual exclusion to more general notions of time and action. As it turns out, the general rules for mutual exclusion reasoning take on a remarkably clean and simple form. However, practical instantiations of them turn out to be messy, and require that we make representation and reasoning choices.

Smith, David E.↗

NASA NTRS2000

Bridging the Gap Between Planning and Scheduling

Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast. Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of M planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems.

Smith, David E.↗

NASA NTRS2000

Generalizing Backtrack-Free Search: A Framework for Search-Free Constraint Satisfaction

Tractable classes of constraint satisfaction problems are of great importance in artificial intelligence. Identifying and taking advantage of such classes can significantly speed up constraint problem solving. In addition, tractable classes are utilized in applications where strict worst-case performance guarantees are required, such as constraint-based plan execution. In this work, we present a formal framework for search-free (backtrack-free) constraint satisfaction. The framework is based on general procedures, rather than specific propagation techniques, and thus generalizes existing techniques in this area. We also relate search-free problem solving to the notion of decision sets and use the result to provide a constructive criterion that is sufficient to guarantee search-free problem solving.

Jonsson, Ari K.↗

NASA NTRS2000

On Reformulating Planning as Dynamic Constraint Satisfaction

In recent years, researchers have reformulated STRIPS planning problems as SAT problems or CSPs. In this paper, we discuss the Constraint-Based Interval Planning (CBIP) paradigm, which can represent planning problems incorporating interval time and resources. We describe how to reformulate mutual exclusion constraints for a CBIP-based system, the Extendible Uniform Remote Operations Planner Architecture (EUROPA). We show that reformulations involving dynamic variable domains restrict the algorithms which can be used to solve the resulting DCSP. We present an alternative formulation which does not employ dynamic domains, and describe the relative merits of the different reformulations.

Frank, Jeremy↗

NASA NTRS1999

Next Generation Remote Agent Planner

In May 1999, as part of a unique technology validation experiment onboard the Deep Space One spacecraft, the Remote Agent became the first complete autonomous spacecraft control architecture to run as flight software onboard an active spacecraft. As one of the three components of the architecture, the Remote Agent Planner had the task of laying out the course of action to be taken, which included activities such as turning, thrusting, data gathering, and communicating. Building on the successful approach developed for the Remote Agent Planner, the Next Generation Remote Agent Planner is a completely redesigned and reimplemented version of the planner. The new system provides all the key capabilities of the original planner, while adding functionality, improving performance and providing a modular and extendible implementation. The goal of this ongoing project is to develop a system that provides both a basis for future applications and a framework for further research in the area of autonomous planning for spacecraft. In this article, we present an introductory overview of the Next Generation Remote Agent Planner. We present a new and simplified definition of the planning problem, describe the basics of the planning process, lay out the new system design and examine the functionality of the core reasoning module.

Jonsson, Ari K.↗

NASA NTRS1999

A Framework for Dynamic Constraint Reasoning Using Procedural Constraints

Many complex real-world decision and control problems contain an underlying constraint reasoning problem. This is particularly evident in a recently developed approach to planning, where almost all planning decisions are represented by constrained variables. This translates a significant part of the planning problem into a constraint network whose consistency determines the validity of the plan candidate. Since higher-level choices about control actions can add or remove variables and constraints, the underlying constraint network is invariably highly dynamic. Arbitrary domain-dependent constraints may be added to the constraint network and the constraint reasoning mechanism must be able to handle such constraints effectively. Additionally, real problems often require handling constraints over continuous variables. These requirements present a number of significant challenges for a constraint reasoning mechanism. In this paper, we introduce a general framework for handling dynamic constraint networks with real-valued variables, by using procedures to represent and effectively reason about general constraints. The framework is based on a sound theoretical foundation, and can be proven to be sound and complete under well-defined conditions. Furthermore, the framework provides hybrid reasoning capabilities, as alternative solution methods like mathematical programming can be incorporated into the framework, in the form of procedures.

Jonsson, Ari K.↗