Mobile Thread Task Manager

The Mobile Thread Task Manager (MTTM) is being applied to parallelizing existing flight software to understand the benefits and to develop new techniques and architectural concepts for adapting software to multicore architectures. It allocates and load-balances tasks for a group of threads that migrate across processors to improve cache performance. In order to balance-load across threads, the MTTM augments a basic map-reduce strategy to draw jobs from a global queue. In a multicore processor, memory may be "homed" to the cache of a specific processor and must be accessed from that processor. The MTTB architecture wraps access to data with thread management to move threads to the home processor for that data so that the computation follows the data in an attempt to avoid L2 cache misses. Cache homing is also handled by a memory manager that translates identifiers to processor IDs where the data will be homed (according to rules defined by the user). The user can also specify the number of threads and processors separately, which is important for tuning performance for different patterns of computation and memory access. MTTM efficiently processes tasks in parallel on a multiprocessor computer. It also provides an interface to make it easier to adapt existing software to a multiprocessor environment

Planning and Execution for an Autonomous Aerobot

The Aerial Onboard Autonomous Science Investigation System (AerOASIS) system provides autonomous planning and execution capabilities for aerial vehicles (see figure). The system is capable of generating high-quality operations plans that integrate observation requests from ground planning teams, as well as opportunistic science events detected onboard the vehicle while respecting mission and resource constraints. AerOASIS allows an airborne planetary exploration vehicle to summarize and prioritize the most scientifically relevant data; identify and select high-value science sites for additional investigation; and dynamically plan, schedule, and monitor the various science activities being performed, even during extended communications blackout periods with Earth

Autonomous Coordination of Science Observations Using Multiple Spacecraft

This software provides capabilities for autonomous cross-cueing and coordinated observations between multiple orbital and landed assets. Previous work has been done in re-tasking a single Earth orbiter or a Mars rover in response to that craft detecting a science event. This work enables multiple spacecraft to communicate (over a network designed for deep-space communications) and autonomously coordinate the characterization of such a science event. This work investigates a new paradigm of space science campaigns where opportunistic science observations are autonomously coordinated among multiple spacecraft. In this paradigm, opportunistic science detections can be cued by multiple assets where a second asset is requested to take additional observations characterizing the identified surface feature or event. To support this new paradigm, an autonomous science system for multiple spacecraft assets was integrated with the Interplanetary Network DTN (Delay Tolerant Network) to provide communication between spacecraft assets. This technology enables new mission concepts that are not feasible with current technology. The ability to rapidly coordinate activities across spacecraft without requiring ground in the loop enables rapid reaction to dynamic events across platforms, such as a survey instrument followed by a targeted high resolution instrument, as well as regular simultaneous observations

Autonomous Exploration for Gathering Increased Science

The Autonomous Exploration for Gathering Increased Science System (AEGIS) provides automated targeting for remote sensing instruments on the Mars Exploration Rover (MER) mission, which at the time of this reporting has had two rovers exploring the surface of Mars (see figure). Currently, targets for rover remote-sensing instruments must be selected manually based on imagery already on the ground with the operations team. AEGIS enables the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. In particular, this technology will be used to automatically acquire sub-framed, high-resolution, targeted images taken with the MER panoramic cameras. This software provides: 1) Automatic detection of terrain features in rover camera images, 2) Feature extraction for detected terrain targets, 3) Prioritization of terrain targets based on a scientist target feature set, and 4) Automated re-targeting of rover remote-sensing instruments at the highest priority target

Integrating explanation-based and inductive learning techniques to acquire search-control for planning

Planning systems have become an important tool for automating a wide variety of tasks. Control knowledge guides a planner to find solutions quickly and is crucial for efficient planning in most domains. Machine learning techniques enable a planning system to automatically acquire domain-specific search-control knowledge for different applications. Past approaches to learning control information have usually employed explanation-based learning (EBL) to generate control rules. Unfortunately, EBL alone often produces overly complex rules that actually decrease rather than improve overall planning efficiency. This paper presents a novel learning approach for control knowledge acquisition that integrates explanation-based learning with techniques from inductive logic programming. In our learning system Scope, EBL is used to constrain an inductive search for control heuristics that help a planner choose between competing plan refinements. Scope is one of the few systems to address learning control information for newer, partial-order planners. Specifically, this proposal describes how Scope learns domain-specific control rules for the UCPOP planning algorithm. The resulting system is shown to produce significant speedup in two different planning domains, and to be more effective than a pure EBL approach. Future research will be performed in three main areas. First, Scope's learning algorithm will be extended to include additional techniques such as constructive induction and rule utility analysis. Second, Scope will be more thoroughly tested; several real-world planning domains have been identified as possible testbeds, and more in-depth comparisons will be drawn between Scope and other competing approaches. Third, Scope will be implemented in a different ..

Integrating EBL and ILP to Acquire Control Rules for Planning

Most approaches to learning control information in planning systems use explanation-based learning to generate control rules. Unfortunately, EBL alone often produces overly complex rules that actually decrease planning efficiency. This paper presents a novel learning approach for control knowledge acquisition that integrates explanation-based learning with techniques from inductive logic programming. EBL is used to constrain an inductive search for selection heuristics that help a planner choose between competing plan refinements. SCOPE is on

Learning to Improve both Efficiency and Quality of Planning

Most research in learning for planning has concentrated on efficiency gains. Another important goal is improving the quality of final plans. Learning to improve plan quality has been examined by a few researchers, however, little research has been done learning to improve both efficiency and quality. This paper explores this problem by using the Scope learning system to acquire control knowledge that improves on both of these metrics. Since Scope uses a very flexible training approach, we can easily focus its learning algorithm to prefer search paths that are better for particular evaluation metrics. Experimental results show that Scope can significantly improve both the quality of final plans and overall planning efficiency. 1 Introduction A considerable amount of planning and learning research has been devoted to improving planning efficiency, also known as "speedup learning" [ Minton, 1989; Leckie and Zuckerman, 1993; Estlin and Mooney, 1996; Kambhampati et al., 1996 ] . These sy..

Hybrid Learning of Search Control for Partial-Order Planning

This paper presents results on applying a version of the Dolphin search-control learning system to speed up a partial-order planner. Dolphin integrates explanation-based and inductive learning techniques to acquire effective clause-selection rules for Prolog programs. A version of th