Planning and scheduling research at NASA Ames Research Center

Planning and scheduling is the area of artificial intelligence research that focuses on the determination of a series of operations to achieve some set of (possibly) interacting goals and the placement of those operations in a timeline that allows them to be accomplished given available resources. Work in this area at the NASA Ames Research Center ranging from basic research in constrain-based reasoning and machine learning, to the development of efficient scheduling tools, to the application of such tools to complex agency problems is described

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture

The embedding of self-organizing inter-agent processes in distributed software applications enables the decentralized coordination system elements, solely based on concerted, localized interactions. The separation and encapsulation of the activities that are conceptually related to the coordination, is a crucial concern for systematic development practices in order to prepare the reuse and systematic integration of coordination processes in software systems. Here, we discuss a programming model that is based on the externalization of processes prescriptions and their embedding in Multi-Agent Systems (MAS). One fundamental design concern for a corresponding execution middleware is the minimal-invasive augmentation of the activities that affect coordination. This design challenge is approached by the activation of agent modules. Modules are converted to software elements that reason about and modify their host agent. We discuss and formalize this extension within the context of a generic coordination architecture and exemplify the proposed programming model with the decentralized management of (web) service infrastructures

A high level e-maintenance architecture to support on-site teams

Emergent architectures and paradigms targeting reconfigurable manufacturing systems increasingly rely on intelligent modules to maximize the robustness and responsiveness of modern installations. Although intelligent behaviour significantly minimizes the occurrence of faults and breakdowns it does not exclude them nor can prevent equipment’s normal wear. Adequate maintenance is fundamental to extend equipments’ life cycle. It is of major importance the ability of each intelligent device to take an active role in maintenance support. Further this paradigm shift towards “embedded intelligence”, supported by cross platform technologies, induces relevant organizational and functional changes on local maintenance teams. On the one hand, the possibility of outsourcing maintenance activities, with the warranty of a timely response, through the use of pervasive networking technologies and, on the other hand, the optimization of local maintenance staff are some examples of how IT is changing the scenario in maintenance. The concept of e-maintenance is, in this context, emerging as a new discipline with defined socio-economic challenges. This paper proposes a high level maintenance architecture supporting maintenance teams’ management and offering contextualized operational support. All the functionalities hosted by the architecture are offered to the remaining system as network services. Any intelligent module, implementing the services’ interface, can report diagnostic, prognostic and maintenance recommendations that enable the core of the platform to decide on the best course of action.manufacturing systems, platform technologies, maintenance

The 2014 International Planning Competition: Progress and Trends

We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems

Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence