This paper outlines a specification for an algorithm-design system (based on previous work involving protocol analysis) and describes an implementation of the specification that is a combination frame and production system. In the implementation, design occurs in two problem spaces -- one about algorithms and one about the task-domain. The partially worked out algorithms are represented as configurations of dataflow components. A small number of general-purpose operators construct and modify the representations. These operators are adapted to different situations by instantiation and means-ends ana,lysis rules. The data-flow space also includes symbolic and test-case execution rules that drive the component-refinement orocess by exposing both problems and opportunities. A domain space about geometric images supports test,case execution, domain-specific problem solving, recognition and discovery.
This paper reports on the findings of an ongoing project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers which experience abrupt, partial or full failure of component devices. The problem we address is: given a hybrid model of system behavior, a history of executed controller actions, and a history of observations, including an observation of behavior that is aberrant relative to the model of expected behavior, determine what fault occurred to have caused the aberrant behavior. Determining a diagnosis can be cast as a search problem to find the most likely model for the data. Unfortunately, the search space is extremely large. To reduce search space size and to identify an initial set of candidate diagnoses, we propose to exploit techniques originally applied to qualitative diagnosis of continuous systems. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA's Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.
Effective knowledge management maintains the knowledge assets of an organization by identifying and capturing useful information in a usable form, and by supporting refinement and reuse of that information in service of the organization's goals. A particularly important asset is the "internal" knowledg embodied in the experiences of task experts that may be lost with shifts in projects and personnel. Concept Mapping provides a framework for making this internal knowledge explicit in a visual form that can easily be examined and shared. However, it does not address how relevant concept maps can be retrieved or adapted to new problems. CBR is playing an increasing role in knowledge retrieval mad reuse for corporate memories, and its capabilities are appealing to augmenthe concept mapping process.
Several years ago, there were reports that an IBM artificial intelligence (AI) project had mimicked the brain of a cat. Being the smartass that I am, I responded on Twitter with, "You mean it spends 18 hours a day in sleep mode?" That report was later debunked, but the effort to simulate the brain continues, using new types of processors far faster and more brain-like than your standard x86 processor. IBM and the U.S. Air Force have announced one such project, while Google has its own. What Google is proposing is a template for how to create a single machine learning model that can address multiple tasks.
We argue that this approach is impossible to follow in many real-world domains. The agent may not have enough information to ensure that an action will have a given effect in advance of executing it. This paper describes PUCCINI, a partialorder planner used to control the Internet Softbot (Etzioni & Weld 1994).