Causal simulation and sensor planning in predictive monitoring

Two issues are addressed which arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to utilize sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. The approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, useful in assessing the relative importance of events so that sensor resources can be allocated effectively. The potential applicability of this work to the execution monitoring of robot task plans is briefly discussed

Representing Change for Common-Sense Physical Reasoning

Change pervades every moment of our lives. Much of our success in dealing with a constantly changing world is based in common-sense physical reasoning about processes and physical systems. Processes are the way quantities interact over time. Physical systems can be described as a set of quantities and the processes that operate on them. Representations for causality, time, and quantity are needed to fully characterize change in this domain. Several ideas for these representations are examined and synthesized in this paper towards the goal of constructing a framework to support understanding of, reasoning about, and learning how things work.MIT Artificial Intelligence Laborator

Aspects of the Rover Problem

The basic task of a rover is to move about automonously in an unknown environment. A working rover must have the following three subsystems which interact in various ways: 1) locomotion--the ability to move, 2) perception--the ability to determine the three-dimensional structure of the environment, and 3) navigation--the ability to negotiate the environment. This paper will elucidate the nature of the problem in these areas and survey approaches to solving them while paying attention to real-world issues.MIT Artificial Intelligence Laborator

Predictive monitoring research: Summary of the PREMON system

Traditional approaches to monitoring are proving inadequate in the face of two important issues: the dynamic adjustment of expectations about sensor values when the behavior of the device is too complex to enumerate beforehand, and the selective but effective interpretation of sensor readings when the number of sensors becomes overwhelming. This system addresses these issues by building an explicit model of a device and applying common-sense theories of physics to model causality in the device. The resulting causal simulation of the device supports planning decisions about how to efficiently yet reliably utilize a limited number of sensors to verify correct operation of the device

Attention focusing and anomaly detection in systems monitoring

Any attempt to introduce automation into the monitoring of complex physical systems must start from a robust anomaly detection capability. This task is far from straightforward, for a single definition of what constitutes an anomaly is difficult to come by. In addition, to make the monitoring process efficient, and to avoid the potential for information overload on human operators, attention focusing must also be addressed. When an anomaly occurs, more often than not several sensors are affected, and the partially redundant information they provide can be confusing, particularly in a crisis situation where a response is needed quickly. The focus of this paper is a new technique for attention focusing. The technique involves reasoning about the distance between two frequency distributions, and is used to detect both anomalous system parameters and 'broken' causal dependencies. These two forms of information together isolate the locus of anomalous behavior in the system being monitored

Early Childhood Intervention. Rationale, Timing and Efficacy

This paper provides a brief review of the economic rationale for investing in early childhood. It discusses the optimal timing of intervention, with reference to recent work in developmental neuroscience, and asks how early is early? It motivates the need for early intervention by providing an overview of the impact of adverse factors during the antenatal and early childhood period on outcomes later in life. Early childhood interventions, even poorly designed ones, are costly to implement, therefore it is vital that interventions are well-designed if they are to yield high economic and social returns. The paper therefore presents a set of guiding principles for the effectiveness of early intervention. It concludes by presenting a case for a new study of the optimal timing of interventions.Early childhood intervention, brain development, optimal timing

Attention focussing and anomaly detection in real-time systems monitoring

In real-time monitoring situations, more information is not necessarily better. When faced with complex emergency situations, operators can experience information overload and a compromising of their ability to react quickly and correctly. We describe an approach to focusing operator attention in real-time systems monitoring based on a set of empirical and model-based measures for determining the relative importance of sensor data

The Visco-Elastic Behavior of a Highly Plasticized Nitrocellulose in Compression under Constant Load

The behavior of a highly plasticized nitrocellulose in compression under constant load has been investigated over a rather wide range of load and temperature. The load dependence appears to require the assumption of non‐Newtonian viscosity. Though the observations are not quantitatively accounted for by the Tobolsky‐Eyring model, the load and temperature dependence of the rate of compression appear to have some relation to the predictions of these authors

ECLSS predictive monitoring

On Space Station Freedom (SSF), design iterations have made clear the need to keep the sensor complement small. Along with the unprecendented duration of the mission, it is imperative that decisions regarding placement of sensors be carefully examined and justified during the design phase. In the ECLSS Predictive Monitoring task, we are developing AI-based software to enable design engineers to evaluate alternate sensor configurations. Based on techniques from model-based reasoning and information theory, the software tool makes explicit the quantitative tradeoffs among competing sensor placements, and helps designers explore and justify placement decisions. This work is being applied to the Environmental Control and Life Support System (ECLSS) testbed at MSFC to assist design personnel in placing sensors for test purposes to evaluate baseline configurations and ultimately to select advanced life support system technologies for evolutionary SSF

Construction and Refinement of Justified Causal Models Through Variable-Level Explanation and Perception, and Experimenting

The competence being investigated is causal modelling, whereby the behavior of a physical system is understood through the creation of an explanation or description of the underlying causal relations. After developing a model of causality, I show how the causal modelling competence can arise from a combination of inductive and deductive inference employing knowledge of the general form of causal relations and of the kinds of causal mechanisms that exist in a domain. The hypotheses generated by the causal modelling system range from purely empirical to more and more strongly justified. Hypotheses are justified by explanations derived from the domain theory and by perceptions which instantiate those explanations. Hypotheses never can be proven because the domain theory is neither complete nor consistent. Causal models which turn out to be inconsistent may be repairable by increasing the resolution of explanation and/or perception. During the causal modelling process, many hypotheses may be partially justified and even leading hypotheses may have only minimal justification. An experiment design capability is proposed whereby the next observation can be deliberately arranged to distinguish several hypotheses or to make particular hypotheses more justified. Experimenting is seen as the active gathering of greater justification for fewer and fewer hypotheses.MIT Artificial Intelligence Laborator