Q2 Symbolic Reasoning about Noisy Dynamic Systems (original) (raw)

Northeastern University College of Engineering Q 2 Symbolic Reasoning about Noisy Dynamic Systems

2008

Symbolic reasoning about continuous dynamic systems requires consistent qualitative abstraction functions and a consistent symbolic model. Classically, symbolic reasoning systems have utilized a box partition of the system space to achieve qualitative abstraction, but boxes can not provide a consistent abstraction. Our Q methodology abstracts a provably consistent symbolic representation of noise-free general dynamic systems. However the Q symbolic representation has not been previously evaluated for efficacy in the presence of noise. We evaluate the effects of noise on Q 2 symbolic reasoning in the domain of maneuver detection. We demonstrate how the Q 2 methodology derives a symbolic abstraction of a general dynamic system model used in evaluating maneuver detectors. Simulation results represented by ROC curves show that the Q 2 based maneuver detector is superior to a box-based detector. While no method is consistent in the presence of noise, the Q 2 methodology is superior to th...

Diagnosability of Input Output Symbolic Transition Systems

2009 First International Conference on Advances in System Testing and Validation Lifecycle, 2009

Diagnosability checking of discrete-event systems has been extensively studied in the framework of classical non symbolic models such as Labeled Transition Systems. It happens that in practice such models tend to need too much space to be efficiently processed. By opposition, symbolic approaches offer an expressive, easy and concise way to model systems, and checking diagnosability from such symbolic models can benefit from this reduction of space complexity. Indeed, though this will generally translate into time complexity, such a tradeoff is advantageous, as diagnosability checking is something that is usually done at design stage. This is why this paper proposes a theoretical framework to check diagnosability of Input Output Symbolic Transition Systems (IOSTS) by adapting the twin plant approach to the symbolic case and relying on the use of a symbolic model checker. This theoretical work is being currently applied to embedded functions inside a vehicle in the context of an industrial project and a simplified version of this problem will serve as a running example throughout the presentation.

A symbolic solution to intelligent real-time control

1993

A symbolic solution to intelligent real-time control, Robotics and Autonomous Systems 11 (1993) 279-291. Autonomous systems must operate in dynamic, unpredictable environments in real time. The task of flying a plane is an example of an environment in which the agent must respond quickly to unexpected events while pursuing goals at different levels of complexity and granularity. We present a system, Air-Soar, that achieves intelligent control through fully symbolic reasoning in a hierarchy of simultaneously active problem spaces. Achievement goals, changing to a new state, and homeostatic goals, continuously maintaining a constraint, are smoothly integrated within the system. The hierarchical approach and support for multiple, simultaneous goals gives rise to multi-level reactive behavior, in which Air-Soar responds to unexpected events at the same granularity where they are first sensed.

Symbolic Decision Theory and Autonomous Systems

2013

The ability to reason under uncertainty and with incomplete information is a fundamental requirement of decision support technology. In this paper we argue that the concentration on theoretical techniques for the evaluation and selection of decision options has distracted attention from many of the wider issues in decision making. Although numerical methods of reasoning under uncertainty have strong theoretical foundations, they are representationally weak and only deal with a small part of the decision process. Knowledge based systems, on the other hand, offer greater flexibility but have not been accompanied by a clear decision theory. We describe here work which is under way towards providing a theoretical framework for symbolic decision procedures. A central proposal is an extended form of inference which we call argumentation; reasoning for and against decision options from generalised domain theories. The approach has been successfully used in several decision support applications, but it is argued that a comprehensive decision theory must cover autonomous decision making, where the agent can formulate questions as well as take decisions. A major theoretical challenge for this theory is to capture the idea of reflection to permit decision agents to reason about their goals, what they believe and why, and what they need to know or do in order to achieve their goals.

A semiqualitative methodology for reasoning about dynamic systems

1999

A new methodology is proposed in this paper in order to study semiqualitative models of dynamic systems. It is also described a formalism to incorporate qualitative information into these models. This qualitative information may be composed of. qualitative operators, envelope functions, qualitative labels and qualitative continuous functions. This methodology allows us to study all the states of a dynamic system: the stationary and the transient states. It also allows us to obtain behaviours patterns of semiqualitative dynamic systems . The main idea of the methodology follows : a semiqualitative model is transformed into a family of quantitative models . Every quantitative model has a different quantitative behaviour, however they may have similar qualitative behaviours .

Reasoning in Logic about Continuous Systems

Principles of Knowledge Representation and Reasoning, 1994

An intelligent agent, reasoning symbolically ill a continuous world, needs to infer properties of the behaviors of continuous systems. A qualitative simulator, such as QSI NI , constructs a set. of possible behaviors corisistent with a qualitative differential equation (QDE) and initial state. This set of behaviors is expressed as a flinte tree of qualitative state descriptions. li-i the case of QS1 lvi, this set is guaranteed to contain the "actual" behavior under certain circumstances. We call this property the "soundness" of QSI lvi. Tile behaviortree call then be interpreted as a model for statenients in a branchingtime telilporal logic such as Expressive Behavior Tree Logic (EBTL) , which we introduce. Because QSl M is sound, validity of an EF3TL proposition (necessarily p) implies the corresponding theorem about the dynaniical system described by the QDE. Therefore, at least for universals, statements in temporal logic about conti iuous systems can he proved by qualitative simulation. This allows a liy-b rid reasoning system to prove such comlnon~sense stat emnelits as "what, goes up (in a constallt gravitational field) must conic down or to do such expert reasoning about dymianiical systems as proving the stability of a 11011-1 inear, heterogeneous controller.

On-Board Autonomy via Symbolic Model-Based Reasoning

2008

Deep space and remote planetary exploration missions are characterized by severely constrained communication links and often require intervention from Ground to overcome the difficulties encountered during the mission. An adequate Ground control could be compromised due to communication delays and required Ground decision-making time, endangering the system, although safing procedures are strictly adhered to. To meet the needs of future missions and increase their scientific return, space systems will require an increased level of autonomy on-board. We propose a solution to on-board autonomy relying on model-based reasoning. Our approach integrates many important functionalities (such as plan generation, plan execution and monitoring, fault detection identification and recovery, and run-time diagnosis) in a uniform formal framework. The spacecraft is equipped with an Autonomous Reasoning Engine (ARE) structured according to a generic three-layer hybrid autonomy architecture: Deliberative, Executive and Control Layers. The ARE uses a symbolic representation of the controlled platform. Reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of the ARE, and we have evaluated it on two case studies inspired by real-world ongoing projects: a planetary rover and an orbiting spacecraft. For each case study, we have used a simulator to characterize the approach in terms of reliability, availability and performances.

Automatic construction of reactive control systems using symbolic machine learning

The Knowledge Engineering Review, 1996

This paper reviews a number of applications of Machine Learning to industrial control problems. We take the point of view of trying to automatically build rule-based reactive systems for tasks that, if performed by humans, would require a high degree of skill, yet are generally performed without thinking. Such skills are said to be sub-cognitive. While this might seem restrictive, most human skill is executed subconsciously and only becomes conscious when an unfamiliar circumstance is encountered. This kind of skill lends itself well to representation by a reactive system, that is, one that does not create a detailed model of the world, but rather, attempts to match percepts with actions in a very direct manner.