Multiple-agent hybrid control architecture for intelligent real-time

Abstract

A Multiple-Agent Hybrid Control Architecture (MAHCA) uses agents to analyze design, and implement intelligent control of distributed processes. A network of agents can be configured to control more complex distributed processes. The network of agents interact to create an emergent global behavior. Global behavior is emergent from individual agent behaviors and is achieved without central control through the imposition of global constraints on the network of individual agent behaviors. Agent synchronization can be achieved by satisfaction of an interagent invariance principle. At each update time, the active plan of each of the agents in the network encodes equivalent behavior modulo a congruence relation determined by the knowledge clauses in each agents's knowledge base. The Control Loop and the Reactive Learning Loop of each agent can be implemented separately. This separation results in an implementation runs faster and with less memory requirements than an unseparated arrangement. A Direct Memory Map (DMM) is to implement the agent architecture. The DMM is a procedure for transforming knowledge and acts as a compiler of agent knowledge by providing a data structure called memory patches, which are used to organize the knowledge contained in each agent's Knowledge Base. Content addressable memory is used as the basic mechanism of the memory patch structure. Content addressable memory uses a specialized register called the comparand to store a pattern that is compared with contents of the memory cells. The DMM has two comparands, the Present State Comparand and the Goal Comparand. The MAHCA can be used for compression/decompression for processing and storage of audio or video data.