We have for several years been working on an approach to knowledge system building that argues for the existence of a close connection between the tasks which the knowledge system is intended to solve, the methods chosen for them and the vocabulary in which knowledge is to be modeled and represented. We trace the historical origins of the idea that we have called Generic Tasks, and outline their evolution and accomplishments based on them. We then critique their original implementations from the perspective of flexible integration. We follow this with an outline of our current generalization of the view in the form of a theory of task structures. We describe the architectural implications of this view and outline some research directions.

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Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. This task is difficult because written descriptions often do not specify exactly how referenced events fit together. This article (1) characterizes the causal reconstruction problem, (2) presents a representation called transition space, which portrays events in terms of "transitions," or collections of changes expressible in everydaylanguage, and (3) describes a program called PATHFINDER, which uses the transition space representation to perform causal reconstruction on simplified English descriptions of physical activity.PATHFINDER works byidentifying partial matches between the representations of events and using these matches to form causal chains, fill causal gaps, and merge overlapping accounts of activity. By applying transformations to events prior to matching, PATHFINDER is also able to handle a range of discontinuities arising from a writer's use of analogy or abstraction.

. I review research in the last decade in the framework called Functional Representation (FR). FR is a language for describing the function of a device, its structure, and the causal processes in the device that culminate in the achievement of the function. The causal transitions are annotated in specific ways that explain the role of the functions of the components and domain laws in the achievement of various states and hence the function of the device. I describe the uses of FR for simulation, diagnosis and design, among other tasks. 1. Introduction Causal reasoning is a major component of the activity of cognitive agents. In order to achieve various goals, the agents have to take actions which change the states of the environment. Synthesizing action plans generally requires making causal models of the environment, i.e., building explanations of "how things work," using such models to hypothesize potentially beneficial actions, and predicting consequences of proposed actions. When...

Models that are constructed within the bounds of a single paradigm are not sufficient for modeling all aspects of complex systems. Therefore, even though reasoning and simulation systems that utilize a single modeling paradigm are the current norm, we explore a multimodel approach in this paper. A multimodel approach is defined as one in which more than one model --- each derived from a different perspective, and utilizing correspondingly distinct reasoning and simulation strategies --- are employed. By describing four models which illustrate the use of different modeling techniques, we show how a multimodel approach can enrich the modeling environment and make it correspond better with real world information. Our models come from many sources --- Systems and Simulation Theory for the modeling of natural phenomenaand artificial devices, and Artificial Intelligence and Cognitive Science for the modeling of human intuition and expertise in reasoning. Generalizing from these four models, we suggest that modeling complex systems may best be approached from an integrated architectural viewpoint which combines multiple modeling paradigms.

Abstract. Static mixed-mode presentations consisting of verbal explanations illustrated with diagrams have long been used to communicate information. With the advent of multimedia, such presentations have become dynamic, by migrating from paper to the computer and by adding interactivity and animation. The conventional wisdom is that computer-based multimedia presentations are better than printed presentations. However, does the communicative power of mixed-mode representations stem from their careful design to match cognitive processes involved in comprehension or from their interactive and animated nature? This is an important issue that has never been investigated. This paper first presents a cognitive model of comprehension of mixed-mode representations. We describe how this model generates design guidelines for mixed-mode representations that present expository material in two domains- the concrete domain of mechanical systems and the abstract domain of computer algorithms. We then report on a series of studies that compared computer-based interactive multimedia presentations and their paper-based counterparts. Both were designed in accordance with the comprehension model and were compared against each other and against competing representational forms such as books, CD-ROMs, and animations. These studies indicate that the effectiveness of mixed-mode presentations has more to do with their match with comprehension processes than the medium of presentation. In other words, benefits of interactivity and animation are likely being overstated in the current milieu of fascination with multimedia.

Abstract. We present a method for constructing a teleological model of a drawing of a physical device through analogical transfer of the teleological model of the same device in an almost identical drawing. A source case, in this method, contains both a 2-D vector-graphics line drawing of a physical device and a teleological model of the device called a Drawing-Shape-Structure-Behavior-Function (DSSBF) model that relates shapes and spatial relations in the drawing to specifications of the structure, behavior and function of the device. Given an almost identical target 2-D vector-graphics line drawing as input, we describe how an agent may align the two drawings, and transfer the relevant structural, behavioral and functional elements over to the target drawing. We also describe how the DSSBF model of the source drawing guides the alignment of the two drawings. The Archytas system implements this method in domain of kinematic devices that convert translational motion into rotational motion, such as a piston and crankshaft device. 1

Technological advances that facilitate the infusion of intelligence and graphics into interfaces provide an important opportunity for research on cognitive aspects of visual representations to influence the design of smart graphic user interfaces to information-rich applications. In this context, we report on a cognitive model of multimodal comprehension and empirical results on interactive multimedia information presentations developed according to this model. These are preliminary results emerging from an ongoing long-term research program investigating cognitive constraints on multimedia information presentation design.

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Spatial reasoning is a diverse topic: what might different spatial tasks have in common? One task where substantial progress has been made is qualitative spatial reasoning about motion. Unlike qualitative dynamics, purely qualitative spatial representations have not proven fruitful. Instead, a diagrammatic representation appears to be necessary. This paper begins by outlining the Metric Diagram/ Place Vocabulary (MD/PV) model of qualitative spatial reasoning, illustrating its power with via two example systems: FROB, a system which reasoned about motion, and CLOCK, a system which analyzed fixed-axis mechanisms. We believe this model is applicable beyond simply reasoning about motion. We suspect that (1) some form of metric diagram is a central unifying factor in all spatial reasoning tasks and (2) for human spatial reasoning, the metric diagram is part of, or at least grounded in, our perceptual apparatus. In this spirit, we identify three other kinds of spatial reasoning tasks as research frontiers where substantial progress might also be made, and pose six challenge problems to serve as milestones. The frontiers are (1) deriving system function from concrete structural descriptions (2) representing and reasoning about spatially distributed systems and (3) explicating the role of visual perception and recognition in spatial reasoning. 1.