Many potential uses of qualitative physics, such as robot planning and intelligent computer-aided engineering, require integrating physics with actions taken by agents. This paper proposes to augment qualitative simulation to include the effects of actions to form action-augmented envisionments. The action-augmented envisionment incorporates both the effects of an agent's actions and what will happen in the physical world whether or not the agent does something. Consequently, it should provide a richer basis for planning and procedure generation than any previous representation. This paper defines actionaugmented envisionments and an algorithm for directly computing them, along with an analysis of its complexity and suitability for different kinds of problems. We describe our initial implementation and discuss potential extensions, including incremental algorithms. Keywords: Qualitative reasoning, planning, artificial intelligence. Presented at the 2nd Qualitative Physics Workshop Pa...

Our concern in this paper is with conjecturing diagnoses to explain what happened to a system, given a theory of system behaviour and some observed (aberrant) behaviour. We characterize what happened by introducing the notion of explanatory diagnoses in the language of the situation calculus. Explanatory diagnosesconjecture sequencesof actions to account for a change in system behaviour. We show that determining an explanatory diagnosis is analogous to the classical AI planning task. As such, we exploit previous results on goal regression in the situation calculus to show that determining an explanatory diagnosis can be achieved by regression followed by theorem proving in the database describing what is known of the initial state of our system. Further, we show that in the case of incomplete information, determining explanatory diagnoses is an abductive plan synthesis task.

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This paper begins with a brief review of SME and MAC/FAC, our simulations of matching and retrieval. Next I lay out several arguments for exploring analogy in the large, including why it is now very feasible and what we can learn by such explorations. A new constraint on cognitive simulations, the Integration Constraint, is proposed: A cognitive simulation of some aspect of analogical processing should be usable as a component in larger-scale cognitive simulations. I believe that the implications of this new constraint for cognitive simulation of analogy are far-reaching. After that, two explorations of larger-scale phenomena are described. First, I describe a theoretical framework in which we model common sense reasoning as an interplay of analogical and first-principles reasoning. Second, I describe how SME and MAC/FAC have been used in a case-based coach that is accessible to engineering thermodynamics students worldwide via electronic mail. These examples show that exploring analogy in the large can provide new insights and new challenges to our simulations. Finally, the broader implications of this approach are discussed.

We describe a method of automatically abducing qualitative models from descriptions of behaviors. We generate, from either quan titative or qualitative data, models in the form of qualitative differen tial equations suitable for use by QSIM. Constraints are generated and filtered both by comparison with the input behaviors and by dimensional analysis. If the user provides complete information on the input behaviors and the dimensions of the input variables, the resulting model is un ique, maximally constrained, and guaranteed to reproduce the input behaviors. If the user provides incomplete information , our method will still generate a model which reproduces the input behaviors, but the model may no longer be un ique. Incompleteness can take several forms: missing dimensions, values of variables, or en tire variables.

Model-based diagnosis (MBD) tackles the problem of troubleshooting systems starting from a description of their structure and function (or behavior). Time is a fundamental dimension in MBD: the behavior of most systems is time-dependent in one way or another. Temporal MBD, however, is a difficult task and indeed many simplifying assumptions have been adopted in the various approaches in the literature. These assumptions concern different aspects such as the type and granularity of the temporal phenomena being modeled, the definition of diagnosis, the ontology for time being adopted. Unlike the atemporal case, moreover, there is no general "theory" of temporal MBD which can be used as a knowledge-level characterization of the problem. In this paper we present a general characterization of temporal model-based diagnosis. We distinguish between different temporal phenomena that can be taken into account in diagnosis and we introduce a modeling language which can capture all such phenomena...

This article describes a method for monitoring and diagnosis of process systems based on three foundational technologies: semi-quantitative simulation, measurement interpretation (tracking), and model-based diagnosis. Compared to existing methods based on fixed-threshold alarms, fault dictionaries, decision trees, and expert systems, several advantages accrue: ffl the physical system is represented in a semi-quantitative model which, unlike a pure numeric model, predicts all possible behaviors that are consistent with the incomplete/imprecise knowledge of the system's devices and processes, ensuring, for example, that a hazardous-but-infrequent behavior will not be overlooked; ffl imprecise knowledge of parameter values and functional relationships (both linear and non-linear) can be expressed in the semi-quantitative model and used during simulation, producing a valid range for each variable; ffl incremental simulation of the model in step with incoming sensor readings, with subseq...

Several approaches have been proposed to deal with time in diagnosis. The goal of this paper is to propose a logical characterization of diagnosis with temporal knowledge, and, specifically, diagnosis with temporal constraints on the evolution of the system to be diagnosed. The characterization is independent of the specific temporal constraint language being used and is an extension of an abductive characterization of atemporal diagnosis. In a companion paper [ 4 ] we discuss a computational characterization of a restriction of the framework, based on the co-operation of an abductive and a temporal reasoner. 1 Introduction The need of taking into account the temporal dimension in model-based diagnosis has been advocated by many researchers (see, e.g., chapter 6 in [ 17 ] ). While a static model describes the correct and/or faulty behavior of a system (or of its components), at least two different (but related) dimensions of time have been considered in the approaches proposed so far:...

Monitors in Intensive Care Units generate large volumes of continuous data which result in information overload for the medical staff. Instead of reasoning with individual data samples of one or more variables, it is better to work with the trend of the data i.e whether the data is increasing, decreasing or steady. We have developed a system which abstracts continuous data into trends; it consists of three consecutive processes: filtering which smooths the data; temporal interpolation which creates simple intervals between consecutive data points; and temporal inference which iteratively merges intervals which share similar characteristics into larger intervals. Our system has been applied both to historical and real-time data. Keywords: Intensive Care, interval identification, temporal interpolation, temporal inference. Submitted to the Special Issue on Temporal Intelligent Information Systems in Medicine, Journal of Intelligent Information Systems. ySchool of Computer and Mathematic...

In practice, automated monitoring of spacecraft relies heavily on limitsensing and simulation. Unfortunately, limit-sensing tends to be too imprecise (missing alarms) and simulation tends to be too precise (false alarms). To help overcome those disadvantages, we present an anytime algorithm called envelope learning and monitoring via error relaxation (ELMER). It can incrementally generate successively tighter hi/low limit functions envelopes, essentially moving from the wide static limits typical of limit-sensing toward the precise predictions of simulations, while avoiding unacceptable false alarm rates. We summarize the techniques and motivations underlying ELMER and illustrate its performance on telemetry data from the NASA spacecraft TOPEX.