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245
Recent Advances in AI Planning
 AI MAGAZINE
, 1999
"... The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as Graphplan and compilers that convert planning problems into propositional CNF formulae for solution via systematic or stochastic SAT methods. Related work on the Deep Space O ..."
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Cited by 129 (0 self)
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The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as Graphplan and compilers that convert planning problems into propositional CNF formulae for solution via systematic or stochastic SAT methods. Related work on the Deep Space One spacecraft control algorithms advances our understanding of interleaved planning and execution. In this survey,we explain the latest techniques and suggest areas for future research.
Adjustable autonomy for humancentered autonomous systems
 on Mars,” in First International Conference of the Mars Society
, 1998
"... We expect a variety of autonomous systems, from rovers to lifesupport systems, to play a critical role in the success of manned Mars missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover ..."
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Cited by 104 (6 self)
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We expect a variety of autonomous systems, from rovers to lifesupport systems, to play a critical role in the success of manned Mars missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover, these systems will need to operate safely in the presence of people and cooperate with them effectively. We call such autonomous systems humancentered in contrast with traditional “blackbox ” autonomous systems. Our goal is to design a framework for humancentered autonomous systems that enables users to interact with these systems at whatever level of control is most appropriate whenever they so choose, but minimize the necessity for such interaction. This paper discusses ongoing research at the NASA Ames Research Center and the Johnson Space Center in developing humancentered autonomous systems that can be used for a manned Mars mission.
ModelBased Programming of Intelligent Embedded Systems and Robotic Space Explorers
 In Proceedings of the IEEE: Special Issue on Modeling and Design of Embedded Software
, 2003
"... This paper develops the Reactive ModelBased Programming Language (RMPL) and its executive, called Titan. RMPL provides the features of synchronous, reactive languages, with the added ability of reading and writing to state variables that are hidden within the physical plant being controlled. Titan ..."
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Cited by 90 (31 self)
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This paper develops the Reactive ModelBased Programming Language (RMPL) and its executive, called Titan. RMPL provides the features of synchronous, reactive languages, with the added ability of reading and writing to state variables that are hidden within the physical plant being controlled. Titan executes an RMPL program using extensive componentbased declarative models of the plant to track states, analyze anomalous situations, and generate novel control sequences. Within its reactive control loop, Titan employs propositional inference to deduce the system's current and desired states, and it employs modelbased reactive planning to move the plant from the current to the desired state
Conflictdirected A* and Its Role in Modelbased Embedded Systems
 Journal of Discrete Applied Mathematics
, 2003
"... Artificial intelligence has traditionally used constraint satisfaction and logic to frame a wide range of problems, including planning, diagnosis, cognitive robotics and embedded systems control. However, many decision making problems are now being reframed as optimization problems, involving a sea ..."
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Cited by 74 (25 self)
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Artificial intelligence has traditionally used constraint satisfaction and logic to frame a wide range of problems, including planning, diagnosis, cognitive robotics and embedded systems control. However, many decision making problems are now being reframed as optimization problems, involving a search over a discrete space for the best solution that satisfies a set of constraints. The best methods for finding optimal solutions, such as A*, explore the space of solutions one state at a time. This paper introduces conflictdirected A*, a method for solving optimal constraint satisfaction problems. Conflictdirected A* searches the state space in best first order, but accelerates the search process by eliminating subspaces around each state that are inconsistent. This elimination process builds upon the concepts of conflict and kernel diagnosis used in modelbased diagnosis[1,2] and in dependencydirected search[36]. Conflictdirected A* is a fundamental tool for building modelbased embedded systems, and has been used to solve a range of problems, including fault isolation[1], diagnosis[7], mode estimation and repair[8], modelcompilation[9] and modelbased programming[10].
Hybrid Bayesian Networks for Reasoning about Complex Systems
, 2002
"... Many realworld systems are naturally modeled as hybrid stochastic processes, i.e., stochastic processes that contain both discrete and continuous variables. Examples include speech recognition, target tracking, and monitoring of physical systems. The task is usually to perform probabilistic inferen ..."
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Cited by 69 (0 self)
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Many realworld systems are naturally modeled as hybrid stochastic processes, i.e., stochastic processes that contain both discrete and continuous variables. Examples include speech recognition, target tracking, and monitoring of physical systems. The task is usually to perform probabilistic inference, i.e., infer the hidden state of the system given some noisy observations. For example, we can ask what is the probability that a certain word was pronounced given the readings of our microphone, what is the probability that a submarine is trying to surface given our sonar data, and what is the probability of a valve being open given our pressure and flow readings. Bayesian networks are
Back to the Future for Consistencybased Trajectory Tracking
 Proceedings of the National Conference on Artificial Intelligence. Menlo Park, CA: AAAI
"... Given a model of a physical process and a sequence of commands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algori ..."
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Cited by 69 (0 self)
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Given a model of a physical process and a sequence of commands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algorithms for incrementally generating approximate belief states for a restricted but relevant class of partially observable Markov decision processes with very large state spaces. The algorithm incrementally generates, rather than revises, an approximate belief state at any point by abstracting and summarizing segments of the likely trajectories of the process. This enables applications to efficiently maintain a partial belief state when it remains consistent with observations and revisit past assumptions about the process’s evolution when the belief state is ruled out. The system presented has been implemented and results on examples from the domain of spacecraft control are presented.
Mode estimation of probabilistic hybrid systems
 In Intl. Conf. on Hybrid Systems: Computation and Control
, 2002
"... Abstract. Modelbased diagnosis and mode estimation capabilities excel at diagnosing systems whose symptoms are clearly distinguished from normal behavior. A strength of mode estimation, in particular, is its ability to track a system’s discrete dynamics as it moves between different behavioral mode ..."
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Cited by 63 (18 self)
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Abstract. Modelbased diagnosis and mode estimation capabilities excel at diagnosing systems whose symptoms are clearly distinguished from normal behavior. A strength of mode estimation, in particular, is its ability to track a system’s discrete dynamics as it moves between different behavioral modes. However, often failures bury their symptoms amongst the signal noise, until their effects become catastrophic. We introduce a hybrid mode estimation system that extracts mode estimates from subtle symptoms. First, we introduce a modeling formalism, called concurrent probabilistic hybrid automata (cPHA), that merge hidden Markov models (HMM) with continuous dynamical system models. Second, we introduce hybrid estimation as a method for tracking and diagnosing cPHA, by unifying traditional continuous state observers with HMM belief update. Finally, we introduce a novel, anytime, anyspace algorithm for computing approximate hybrid estimates. 1
Formal verification of diagnosability via symbolic model checking
 In Proceedings of the 18th International Joint Conference on Artificial Intelligence IJCAI’03
, 2003
"... This paper addresses the formal verification of diagnosis systems. We tackle the problem of diagnosability: given a partially observable dynamic system, and a diagnosis system observing its evolution over time, we discuss how to verify (at design time) if the diagnosis system will be able to infer ( ..."
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Cited by 54 (8 self)
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This paper addresses the formal verification of diagnosis systems. We tackle the problem of diagnosability: given a partially observable dynamic system, and a diagnosis system observing its evolution over time, we discuss how to verify (at design time) if the diagnosis system will be able to infer (at runtime) the required information on the hidden part of the dynamic state. We tackle the problem by looking for pairs of scenarios that are observationally indistinguishable, but lead to situations that are required to be distinguished. We reduce the problem to a model checking problem. The finite state machine modeling the dynamic system is replicated to construct such pairs of scenarios; the diagnosability conditions are formally expressed in temporal logic; the check for diagnosability is carried out by solving a model checking problem. We focus on the practical applicability of the method. We show how the formalism is adequate to represent diagnosability problems arising from a significant, realworld application. Symbolic model checking techniques are used to formally verify and incrementally refine the diagnosability conditions. 1
Structure and Complexity in Planning with Unary Operators
 Journal of Artificial Intelligence Research
, 2003
"... Unary operator domains  i.e., domains in which operators have a single effect  arise naturally in many control problems. In its most general form, the problem of strips planning in unary operator domains is known to be as hard as the general strips planning problem  both are pspacecomplete. H ..."
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Cited by 53 (10 self)
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Unary operator domains  i.e., domains in which operators have a single effect  arise naturally in many control problems. In its most general form, the problem of strips planning in unary operator domains is known to be as hard as the general strips planning problem  both are pspacecomplete. However, unary operator domains induce a natural structure, called the domain's causal graph. This graph relates between the preconditions and effect of each domain operator. Causal graphs were exploited by Williams and Nayak in order to analyze plan generation for one of the controllers in NASA's DeepSpace One spacecraft. There, they utilized the fact that when this graph is acyclic, a serialization ordering over any subgoal can be obtained quickly. In this paper we conduct a comprehensive study of the relationship between the structure of a domain's causal graph and the complexity of planning in this domain. On the positive side, we show that a nontrivial polynomial time plan generation algorithm exists for domains whose causal graph induces a polytree with a constant bound on its node indegree. On the negative side, we show that even plan existence is hard when the graph is a directedpath singly connected DAG.
Particle filters for realtime fault detection in planetary rovers
 In Proceedings of the Thirteenth International Workshop on Principles of Diagnosis
, 2002
"... Planetary rovers provide a considerable challenge for robotic systems in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have onboard fault detection and diagnosis capabilities in order to determine the actual state of the ..."
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Cited by 53 (7 self)
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Planetary rovers provide a considerable challenge for robotic systems in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have onboard fault detection and diagnosis capabilities in order to determine the actual state of the vehicle, and decide what actions are safe to perform. Traditional modelbased diagnosis techniques are not suitable for rovers due to the tight coupling between the vehicle’s performance and its environment. Hybrid diagnosis using particle filters is presented as an alternative, and its strengths and weaknesses are examined. We also present some extensions to particle filters that are designed to make them more suitable for use in diagnosis problems. 1