The Companion cognitive architecture supports experiments in achieving human-level intelligence. The seven key features of cognitive architecture include analogical processing, extensive conceptual knowledge, flexible reasoning, and coarse-grained distributed implementation, broad learning at multiple levels, continuous operation, and natural interaction. The model for analogical matching is the Structure-Mapping Engine (SME) that computes mappings using algorithm, operating in polynomial time. The model for similarity-based retrieval is many are called/few are chosen (MAC/FAC) that takes as input a probe and a case library. Another design goal for Companions is to emulate the parallelism that's evident in human behavior. Companions are implemented as distributed systems that allocate individual nodes of a cluster computer to semi-independent, asynchronous processes (agents). Agents communicate internally using the Knowledge Query and Manipulation Language (KQML) with callbacks to support asynchronous queries and subscriptions to events.
ASJC Scopus subject areas
- Computer Networks and Communications
- Artificial Intelligence