Cognitive structures are the foundation of Jean Piaget’s Genetic Epistemology. Yet the elusive question remains: “What are Piaget’s cognitive structures?” and more importantly, “How can they be represented computationally?” Piaget described the monad as an immaterial, weightless, dimensionless entity, while he referred to a scheme as both process and structure. This paper explores an approach to combining the notions of monad and scheme to create a simple knowledge representation. Building upon the work of several authors, notably Jean Piaget, Ryszard Michalski, and Roland Hausser, the neural proposition is the central cognitive structure of the PAM-P2 cognitive system.
How many times have you switched your mobile phone service provider when the service or support was poor? How hard did that service provider work to keep you? It's likely they didn't try very hard. They have many customers, so losing one isn't that big of a deal. But for companies that provide complex products like those in manufacturing, aerospace or oil and gas, a high-quality customer support program is critical.
Gister supports the rapid development of evidential reasoning systems through an interactive, menu-driven, graphical interface, based upon Grasper-CL. The user interacts with the system in much the same way as with electronic spreadsheets, by simply selecting from menus to add evidential operations to an analysis, to modify data or operation parameters, or to change any portion of the uncertain knowledge base. In response, gister updates its analyses to reflect the new information. Gister supports a wide range of evidential operations, including fusion, source discounting, time projection, summarization, evidence interpretation, and sensitivity analysis. Gister has been applied to a wide range of problems, including multisensor interpretation, mission planning, medical diagnosis, intelligence analysis, underwater vehicle tracking, antiair threat identification, robot vehicle navigation, and management decision support.
We are developing Companion Cognitive Systems, a new kind of software that can be effectively treated as a collaborator. Aside from their potential utility, we believe this effort is important because it focuses on three key problems that must be solved to achieve human-level AI: Robust reasoning and learning, interactivity, and longevity. We describe the ideas we are using to develop the first architecture for Companions: analogical processing, grounded in cognitive science for reasoning and learning, sketching and concept maps to improve interactivity, and a distributed agent architecture hosted on a cluster to achieve performance and longevity. We outline some results on learning by accumulating examples derived from our first experimental version. What is known about cognition has grown significantly, and communication between fields in cognitive science has catalyzed all of them. Largescale representational resources, such as WordNet, FrameNet, and Cyc, have become available so that one can build large knowledge systems without starting from scratch. Central processing units (CPUs) have become fast enough and memories large enough to tackle systems that could only be dreamed about previously. The confluence of these three factors suggest to us that the time is right for more ambitious projects, building integrated systems using the best available results from cognitive science. The effort we have embarked on to create Companion Cognitive Systems represents one such project. Let us start with our practical goals for Companions. The problems we face are growing more complex, but we are not becoming any smarter. Software can help, but often it becomes part of the problem by adding new layers of complexity. We need to bring software closer to us, improving conceptual bandwidth and having it adapt to us, rather than the other way around. Our vision is this: Companions will be software aide-de-camps, collaborators with their users. Companions will help their users work through complex arguments, automatically retrieving relevant precedents, providing cautions and counter-indications as well as supporting evidence. Companions will be capable of effective operation for weeks and months at a time, assimilating new information, generating and maintaining scenarios and predictions. Companions will continually adapt and learn, about the domains they are working in, their users, and themselves.
Most of the research in the digital library domain has addressed indexing and recovering digital resources. However, more work is still needed to find effective ways to support users when they actually use (read) digital libraries' documents and to enhance reading experience by sharing digital annotations. In this paper, we present a new system, named SHASS (for SHarable Annotation Support System), providing advanced assistance for making and exploiting annotations in digital libraries.