Knowledge Beads – Approach for Knowledge Representation and Market Place Negotiations for Internet Agents

Abstract

Knowledge Representation can be modeled using rule-based semantics with self-learning capabilities based on given feedback. There are 3 approaches expounded in this paper to achieve this model – a) knowledge-bead b) bill-of-knowledge and c) self learning knowledge mapping. Knowledge-Bead is the fundamental building blocks for rule-based systems that allow for synchronised attribute analysis and comparisons in an agent driven negotiation process. The bill of knowledge is the structure for collections of knowledge-beads that will allow inheritance of rules and attributes. This approach allows probabilistic weightage based knowledge maps so that an infinite level of states can be modelled. This model fits well with the negotiation process is made up of continuous non-binary states. We are also proposing alternative modesl for market place negotiations using Sensing Agent.