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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

SafeMAP: Safe Multi-Agent Planning framework based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Mohammad Hejase↗
NASA NTRS2001

A component based implementation of agents and brokers for design coordination

NASA's mission design coordination has been based on expert opinion of parametric data presented in Excel or Powerpoint. Common access is required to more powerful design tools supporting performance simulation and analysis. Components provide the means for inexpensively adding the desired functionality.

agents components↗
NASA NTRS1990

Simulation-based intelligent robotic agent for Space Station Freedom

A robot control package is described which utilizes on-line structural simulation of robot manipulators and objects in their workspace. The model-based controller is interfaced with a high level agent-independent planner, which is responsible for the task-level planning of the robot's actions. Commands received from the agent-independent planner are refined and executed in the simulated workspace, and upon successful completion, they are transferred to the real manipulators.

Biegl, Csaba A.↗
NASA NTRS1990

A knowledge base architecture for distributed knowledge agents

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

Riedesel, Joel↗
NASA NTRS2020

Projection-Based Inter-Agent Collision Avoidance in Dual Agent Systems

Inter-agent collisions can occur in otherwise dynamically-stable (i.e., Lyapunov stable) dual-agent leaderfollower systems. These inter-agent collisions between the leader and the follower happen during the transient phase of the system’s evolution, although the steady-state behavior of the system is asymptotically/exponentially stable. Therefore, to avoid such inter-agent collisions, it is essential to control the relative error trajectory between the leader and the follower during the transient phase of the system’s evolution. In this paper, we introduce a novel projection operator based modelreference control architecture that can mitigate impending inter-agent collisions by modifying the transient dynamics of relative trajectories. This controller augments the follower’s baseline controller and consists of two essential components: a collision-free reference model based on the projection operator and a model reference tracking controller to guide the follower to follow the reference-model. This paper defines the concept of transient-instability in leader-follower systems, introduces collision mitigation controller architecture, and presents an illustrative example demonstrating its effectiveness.

Bayard, David S.↗
NASA NTRS2005

A Comparative Study of Probability Collectives Based Multi-agent Systems and Genetic Algorithms

We compare Genetic Algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach works in both continuous and discrete problems. It does not suffer from the resolution limitation of the finite bit length encoding of parameters into GA alleles. It also has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems. It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.

Huang, Chien-Feng↗
NASA NTRS2004

Applications for Mission Operations Using Multi-agent Model-based Instructional Systems with Virtual Environments

This viewgraph presentation provides an overview of past and possible future applications for artifical intelligence (AI) in astronaut instruction and training. AI systems have been used in training simulation for the Hubble Space Telescope repair, the International Space Station, and operations simulation for the Mars Exploration Rovers. In the future, robots such as may work as partners with astronauts on missions such as planetary exploration and extravehicular activities.

Clancey, William J.↗
NASA NTRS2009

Assessment of Composite Delamination Self-Healing Under Cyclic Loading

Recently, the promise of self-healing materials for enhanced autonomous durability has been introduced using a micro-encapsulation technique where a polymer based healing agent is encapsulated in thin walled spheres and embedded into a base polymer along with a catalyst phase. For this study, composite skin-stiffener flange debonding specimens were manufactured from composite prepreg containing interleaf layers with a polymer based healing agent encapsulated in thin-walled spheres. Constant amplitude fatigue tests in three-point bending showed the effect of self-healing on the fatigue response of the skin-stiffener flange coupons. After the cycling that created debonding, fatigue tests were held at the mean load for 24 hours. For roughly half the specimens tested, when the cyclic loading was resumed a decrease in compliance (increase in stiffness) was observed, indicating that some healing had occurred. However, with continued cycling, the specimen compliance eventually increased to the original level before the hold, indicating that the damage had returned to its original state. As was noted in a prevoius study conducted with specimens tested under monotonically increasing loads to failure, healing achieved via the micro-encapsulation technique may be limited to the volume of healing agent available relative to the crack volume.

O'Brien, T. Kevin↗
NASA NTRS2002

CATS-based Air Traffic Controller Agents

This report describes intelligent agents that function as air traffic controllers. Each agent controls traffic in a single sector in real time; agents controlling traffic in adjoining sectors can coordinate to manage an arrival flow across a given meter fix. The purpose of this research is threefold. First, it seeks to study the design of agents for controlling complex systems. In particular, it investigates agent planning and reactive control functionality in a dynamic environment in which a variety perceptual and decision making skills play a central role. It examines how heuristic rules can be applied to model planning and decision making skills, rather than attempting to apply optimization methods. Thus, the research attempts to develop intelligent agents that provide an approximation of human air traffic controller behavior that, while not based on an explicit cognitive model, does produce task performance consistent with the way human air traffic controllers operate. Second, this research sought to extend previous research on using the Crew Activity Tracking System (CATS) as the basis for intelligent agents. The agents use a high-level model of air traffic controller activities to structure the control task. To execute an activity in the CATS model, according to the current task context, the agents reference a 'skill library' and 'control rules' that in turn execute the pattern recognition, planning, and decision-making required to perform the activity. Applying the skills enables the agents to modify their representation of the current control situation (i.e., the 'flick' or 'picture'). The updated representation supports the next activity in a cycle of action that, taken as a whole, simulates air traffic controller behavior. A third, practical motivation for this research is to use intelligent agents to support evaluation of new air traffic control (ATC) methods to support new Air Traffic Management (ATM) concepts. Current approaches that use large, human-in-the-loop simulations are unquestionably valuable for this purpose, but pose considerable logistical, fiscal, and experimental control problems. First, data analysis is extremely complicated, owing simply to the large number of participants and data sources in such simulations. In addition, experienced human air traffic controllers working adjacent sectors tend to flexibly adapt to the evolving control problem - potentially shifting to other strategies than those under investigation. In addition, their performance is tightly coupled to the control interface, which in the development phase may support some concepts and supporting strategies better than others. A simple shift in strategy by one controller can change the character of a particular traffic scenario dramatically, which makes experimental comparison of ATC performance under different traffic scenarios difficult. Training a given team of controllers on operations under a new ATM concept for a sufficient period of time could avert such difficulties, but instituting an adequate training program is expensive and logistically difficult.

Callantine, Todd J.↗
NASA NTRS2011

Router Agent Technology for Policy-Based Network Management

This innovation can be run as a standalone network application on any computer in a networked environment. This design can be configured to control one or more routers (one instance per router), and can also be configured to listen to a policy server over the network to receive new policies based on the policy- based network management technology. The Router Agent Technology transforms the received policies into suitable Access Control List syntax for the routers it is configured to control. It commits the newly generated access control lists to the routers and provides feedback regarding any errors that were faced. The innovation also automatically generates a time-stamped log file regarding all updates to the router it is configured to control. This technology, once installed on a local network computer and started, is autonomous because it has the capability to keep listening to new policies from the policy server, transforming those policies to router-compliant access lists, and committing those access lists to a specified interface on the specified router on the network with any error feedback regarding commitment process. The stand-alone application is named RouterAgent and is currently realized as a fully functional (version 1) implementation for the Windows operating system and for CISCO routers.

Chow, Edward T.↗
NASA NTRS2008

Assessment of Composite Delamination Self-Healing Via Micro-Encapsulation

Composite skin/stringer flange debond specimens manufactured from composite prepreg containing interleaf layers with a polymer based healing agent encapsulated in thin walled spheres were tested. As a crack develops and grows in the base polymer, the spheres fracture releasing the healing agent. The agent reacts with catalyst and polymerizes healing the crack. In addition, through-thickness reinforcement, in the form of pultruded carbon z-pins were included near the flange tips to improve the resistance to debonding. Specimens were manufactured with 14 plies in the skin and 10 plies in the stiffener flange. Three-point bend tests were performed to measure the skin/stiffener debonding strength and the recovered strength after healing. The first three tests performed indicated no healing following unloading and reloading. Micrographs showed that delaminations could migrate to the top of the interleaf layer due to the asymmetric loading, and hence, bypass most of the embedded capsules. For two subsequent tests, specimens were clamped in reverse bending before reloading. In one case, healing was observed as evidenced by healing agent that leaked to the specimen edge forming a visible "scar". The residual strength measured upon reloading was 96% of the original strength indicating healing had occurred. Hence, self-healing is possible in fiber reinforced composite material under controlled conditions, i.e., given enough time and contact with pressure on the crack surfaces. The micro-encapsulation technique may prove more robust when capsule sizes can be produced that are small enough to be embedded in the matrix resin without the need for using an interleaf layer. However, in either configuration, the amount of healing that can occur may be limited to the volume of healing agent available relative to the crack volume that must be filled.

O'Brien, T. Kevin↗
NASA NTRS1999

An Evolvable Multi-Agent Approach to Space Operations Engineering

A complex system of spacecraft and ground tracking stations, as well as a constellation of satellites or spacecraft, has to be able to reliably withstand sudden environment changes, resource fluctuations, dynamic resource configuration, limited communication bandwidth, etc., while maintaining the consistency of the system as a whole. It is not known in advance when a change in the environment might occur or when a particular exchange will happen. A higher degree of sophistication for the communication mechanisms between different parts of the system is required. The actual behavior has to be determined while the system is performing and the course of action can be decided at the individual level. Under such circumstances, the solution will highly benefit from increased on-board and on the ground adaptability and autonomy. An evolvable architecture based on intelligent agents that communicate and cooperate with each other can offer advantages in this direction. This paper presents an architecture of an evolvable agent-based system (software and software/hardware hybrids) as well as some plans for further implementation.

Mandutianu, Sanda↗
NASA NTRS2003

Source Update Capture in Information Agents

In this paper we present strategies for successfully capturing updates at Web sources. Web-based information agents provide integrated access to autonomous Web sources that can get updated. For many information agent applications we are interested in knowing when a Web source to which the application provides access, has been updated. We may also be interested in capturing all the updates at a Web source over a period of time i.e., detecting the updates and, for each update retrieving and storing the new version of data. Previous work on update and change detection by polling does not adequately address this problem. We present strategies for intelligently polling a Web source for efficiently capturing changes at the source.

Ashish, Naveen↗
NASA NTRS2003

Environmentally Safer, Less Toxic Fire-Extinguishing Agents

Fire-extinguishing agents comprising microscopic drops of water microencapsulated in flame-retardant polymers have been proposed as effective, less toxic, non-ozone-depleting, non-globalwarming alternatives to prior fire-extinguishing agents. Among the prior fire-extinguishing agents are halons (various halocarbon fluids), which are toxic and contribute both to depletion of upperatmospheric ozone and to global warming. Other prior fire-extinguishing agents are less toxic and less environmentally harmful but, in comparison with halons, are significantly less effective in extinguishing fires. The proposal to formulate new waterbased agents is based on recent success in the use of water mist as a fire-suppression agent. Water suppresses a flame by reducing the flame temperature and the concentration of oxygen available for the combustion process. The temperature is reduced because the water droplets in the mist absorb latent heat of vaporization as they evaporate. The concentration of oxygen is reduced because the newly generated water vapor displaces air. Unfortunately, water mists are difficult to produce in confined spaces and can evaporate before they reach the bases of flames. The proposal addresses both of these issues: The proposed fire-extinguishing agents would be manufactured in microencapsulated form in advance, eliminating the problem of generating mists in confined spaces. Because of the microencapsulation, the droplets would not evaporate until exposed directly to the heat of flames. In addition, the proposal calls for the introduction of free radicals that would inhibit the propagation of the chemical reactions of the combustion reactions. Manufacturing of a fire-extinguishing agent according to the proposal would begin with the formulation of a suitable polymer (e.g., a polybromostyrene) that would contribute free radicals to the combustion process. The polymer would be dissolved in a suitable hydrocarbon liquid (e.g., toluene). Water would be dispersed in the polymer/toluene solution, then another hydrocarbon liquid (e.g., hexane) that is not a solvent for the polymer would be added to the mixture to make the dissolved polymer precipitate onto the water droplets. The resulting polymer-coated droplets would be removed from the coating mixture by filtration, dried, and stored for use.

Parrish, Clyde F.↗
NASA NTRS2024

An On-Board Off-Board Framework for Online Replanning: Applied to UAVs in Urban Environments

Autonomous systems are being used in a multitude of areas at an increasing rate and require a high level of adaptivity and intelligence to operate safely, especially under faulty conditions. This paper introduces a novel genetic algorithm tailored for UAV trajectory replanning, with an improved execution time via search space reduction based on the operating conditions of the UAV and its remaining mission. A unique characteristic of the replanning agent is its fast-start and adaptive properties, pre-seeding candidates with partial solutions and dynamically tuning elitism, crossover, and mutation rates in correspondence to the average fitness and diversity of the population. A population restart mechanism and early stopping mechanism are evaluated as well to assess their effect on solution quality and runtime. Previous work on genetic algorithms for UAV replanning were conducted with short trajectories in a small state space. Our UAV operates in a 56,000 square meter simulated urban environment, with static obstacles and a total of 53 possible waypoints. The agent increases the safety and reliability of UAV autonomy when operating under faulty conditions and when replanning is required.

Machine Learning↗
NASA NTRS2006

Agent Reward Shaping for Alleviating Traffic Congestion

Traffic congestion problems provide a unique environment to study how multi-agent systems promote desired system level behavior. What is particularly interesting in this class of problems is that no individual action is intrinsically "bad" for the system but that combinations of actions among agents lead to undesirable outcomes, As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. This problem is ubiquitous in various traffic problems, including selecting departure times for commuters, routes for airlines, and paths for data routers. In this paper we present a multi-agent approach to two traffic problems, where far each driver, an agent selects the most suitable action using reinforcement learning. The agent rewards are based on concepts from collectives and aim to provide the agents with rewards that are both easy to learn and that if learned, lead to good system level behavior. In the first problem, we study how agents learn the best departure times of drivers in a daily commuting environment and how following those departure times alleviates congestion. In the second problem, we study how agents learn to select desirable routes to improve traffic flow and minimize delays for. all drivers.. In both sets of experiments,. agents using collective-based rewards produced near optimal performance (93-96% of optimal) whereas agents using system rewards (63-68%) barely outperformed random action selection (62-64%) and agents using local rewards (48-72%) performed worse than random in some instances.

Tumer, Kagan↗
NASA NTRS2011

Entanglement in Self-Supervised Dynamics

A new type of correlation has been developed similar to quantum entanglement in self-supervised dynamics (SSD). SSDs have been introduced as a quantum-classical hybrid based upon the Madelung equation in which the quantum potential is replaced by an information potential. As a result, SSD preserves the quantum topology along with superposition, entanglement, and wave-particle duality. At the same time, it can be implemented in any scale including the Newtonian scale. The main properties of SSD associated with simulating intelligence have been formulated. The attention with this innovation is focused on intelligent agents interaction based upon the new fundamental non-New tonian effect; namely, entanglement.

Zak, Michail↗