For more effective collaboration, users and autonomous systems should interact naturally. We propose that sketch-based interaction coupled with qualitative representations and analogy provides a natural interface for users and systems. We introduce comic graphs that capture tasks in terms of the temporal dynamics of the spatial configurations of relevant objects. This paper demonstrates, through a strategy simulation example, how these models could be learned by demonstration, transferred to new situations, and enable explanations.

Qualitative reasoning can play an important role in early stage design. Currently, engineers explore the design space using simulation models built in languages such as Modelica. To make qualitative reasoning useful to them, designs specified in their languages must be translated into a qualitative modeling language for analysis. The contribution of this paper is a sound and effective mapping between Modelica and qualitative reasoning. To achieve a sound mapping, we extend envisioning, the process of generating all relevant qualitative behaviors, to support Modelica's declarative events. For an effective mapping, we identify three classes of additional constraints that should be inferred from the Modelica representation thereby exponentially reducing the number of unrealizable trajectories. We support this contribution with examples and a case study.

Therefore, diagnostic algorithms must reason backwards from symptoms to causes. For example, determining that a dead battery is the cause of your car not starting in the morning (and not the wiring or the ignition switch). The domains of diagnostic algorithms includes analog and digital circuits, software systems, thermal systems, biological systems, and physical mechanisms. The same classes of diagnostic algorithms can apply in all domains. Diagnostic algorithms make observations, often in real time, of a system being diagnosed.

A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems, which move away from the idea of a fixed factory line executing an unchanging set of operations, and toward the goal of an adaptable factory structure. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving on-line reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. We describe the implementation of this design in a prototype highly modular, parallel printing system.

We believe that these goals can be attained through the use of a very high level of modularity, both in hardware and software, combined with intelligent software. To test this hypothesis, Palo Alto Research Center (PARC) designed and built a prototype highly modular system in the printing domain. This "hypermodular" printer explores the extremes of modularity, reconfigurability, and parallelism in both hardware and software. The hardware prototype connects four standard Xerox marking engines (the component of a printer that does the actual printing) in parallel using a highly modular paper path. This configuration can achieve a print rate of four times that of an individual print engine. Reconfigurable manufacturing systems supports flexibility in configuration, graceful degradation (RMSs) were introduced as a concept in the late under component failure, and rerouting of inprocess 1990s (Koren et al. 1999), but the prerequisites, in sheets under exception conditions. These both software and hardware, for implementing them capabilities were made possible by utilizing advanced successfully have proved daunting; very few examples AI techniques in model-based planning, scheduling, of RMSs exist today in practice. These prerequisites search, and temporal reasoning such as state-space include modular, reconfigurable hardware components regression planning, partial-order scheduling, temporal as well as the software and control planning graph-based heuristic estimates, multiobjective architectures and logic to support them. RMSs can search, and fast, simple temporal network include both hard reconfigurability (physical reconfiguration) reasoning. The AI planner / scheduler incorporates and soft reconfigurability (logical reconfiguration) mostly domain-independent techniques from the (ElMaraghy 2006). This latter concept planning and scheduling research community, includes the idea of flexible routing as well as replanning enabling its flexibility and configurability to be and rescheduling.

We believe that the flexibility and robustness of common sense reasoning comes from analogical reasoning, learning, and generalization operating over massive amounts of experience. Million-fact knowledge bases are a good starting point, but are likely to be orders of magnitude smaller, in terms of ground facts, than will be needed to achieve human-like common sense reasoning. This paper describes the FIRE reasoning engine which we have built to experiment with this approach. We discuss its knowledge base organization, including coarse-coding via mentions and a persistent TMS to achieve efficient retrieval while respecting the logical environment formed by contexts and their relationships in the KB. We describe its stratified reasoning organization, which supports both reflexive reasoning (Ask, Query) and deliberative reasoning (Solve, HTN planner). Analogical reasoning, learning, and generalization are supported as part of reflexive reasoning. To show the utility of these ideas, we describe how they are used in the Companion cognitive architecture, which has been used in a variety of reasoning and learning experiments.

Complex electro-mechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. This has become an accepted part of the practice of Xerox, and the control software is deployed in high-end Xerox printers.

Complex electro-mechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. The first is how to make the overall control architecture adapt to, and use productively, the inclusion of particular modules. The second is to decide, at design time, whether a proposed module is a worthwhile addition to the system: will the resulting system perform enough better to outweigh the costs of including the module? This article indicates how the use of qualitative, constraint-based models provides support for solving both of these problems. This has become an accepted part of the practice of Xerox, and the control software is deployed in high-end Xerox printers.