A Novel Learning and Response Generating Agent-based Model for Symbolic - Numeric Knowledge Modeling and Combination

Many modern applications require both modeling and generative capabilities, so that they can produce novel outcomes that address requirements beyond the solutions used in model training. Current AI approaches arguably emphasize modeling but pay much less attention to generative capabilities. This paper presents a new learning and response generating (LRG) agent-based model, in which interacting agents continuously learn symbolic - numeric knowledge and create new outcomes (responses) using a set of five ways to combine concepts. Each way has both fast, reactive and a slow, planned versions. Experiments present the characteristics of an agent's modeling and generating capabilities.