Narrow Results

This paper deals with the problem of distributed causal model-based diagnosis on interacting Behavioral Petri Nets (BPNs). The system to be diagnosed comprises different interacting subsystems (each modeled as a BPN) and the diagnostic system is defined as a multi-agent system where each agent is designed to diagnose a particular subsystem on the basis of its local model, the local received observation and the information exchanged with the neighboring agents. The interactions between subsystems are captured by tokens that may pass from one net model to another via bordered places. The diagnostic reasoning scheme is accomplished locally within each agent by analyzing the P-invariants of the corresponding BPN model. Once local diagnoses are obtained, agents begin to communicate to ensure that such diagnoses are consistent and recover completely the results obtained by a centralized agent having a global view about the whole system.

CAUSA is a knowledge acquisition tool which supports the incremental modeling of complex dynamic systems during the whole knowledge acquisition task. It provides an environment for the modeling and simulation of dynamic systems on a quantitative level. The environment provides a conceptual framework which includes primitives like objects, processes, and causal dependencies, allowing for the modeling of a broad class of complex systems. Simulation allows for the quantitative and qualitative inspection and empirical investigation of the behavior of the modeled system. CAUSA is implemented in Knowledge-Craft and runs on a Symbolics 3640.

General relativity theory (GRT) tells us that (a) space and time should be viewed as an entity (called spacetime), (b) the spacetime of a world that contains gravitational objects should be viewed as curved, and (c) spacetime is a dynamical object with a dynamically changing extent and curvature. Attempts to achieve compatibility of GRT with quantum theory (QT) have typically resulted in proposing elementary units of spacetime as building blocks for the emergence of larger spacetime objects. In the present paper, a model of curved discrete spacetime is presented in which the basic space elements are derived from Causal Dynamical Triangulation. Spacetime can be viewed as the container for physical objects, and in GRT, the energy distribution of the contained physical objects determines the dynamics of spacetime. In the proposed model of curved discrete spacetime, the primary objects contained in spacetime are “quantum objects”. Other larger objects are collections of quantum objects. This approach results in an accordance of GRT and quantum (field) theory, while coincidently the areas in which their laws are in force are separated. In the second part of the paper, a rough mapping of quantum field theory to the proposed model of spacetime dynamics is described.

CAUSA is a knowledge acquisition tool which supports the incremental modeling of complex dynamic systems during the whole knowledge acquisition task. It provides an environment for the modeling and simulation of dynamic systems on a quantitative level. The environment provides a conceptual framework which includes primitives like objects, processes, and causal dependencies, allowing for the modeling of a broad class of complex systems. Simulation allows for the quantitative and qualitative inspection and empirical investigation of the behavior of the modeled system. CAUSA is implemented in Knowledge-Craft and runs on a Symbolics 3640.