Multi-Agent Systems based on Contextual Defeasible Logic considering Focus
This work addresses the problem of representing dynamic and knowledge-intensive scenarios in multi-agent systems for researchers in distributed AI, though it appears incremental as an extension of prior CDL models.
The authors tackled the limitations of existing distributed reasoning models by proposing a multi-agent system based on Contextual Defeasible Logic that allows agents to reason with relevant, unknown knowledge (focus) in dynamic environments, presenting formalizations and an initial BDI-based implementation.
In this paper, we extend previous work on distributed reasoning using Contextual Defeasible Logic (CDL), which enables decentralised distributed reasoning based on a distributed knowledge base, such that the knowledge from different knowledge bases may conflict with each other. However, there are many use case scenarios that are not possible to represent in this model. One kind of such scenarios are the ones that require that agents share and reason with relevant knowledge when issuing a query to others. Another kind of scenarios are those in which the bindings among the agents (defined by means of mapping rules) are not static, such as in knowledge-intensive and dynamic environments. This work presents a multi-agent model based on CDL that not only allows agents to reason with their local knowledge bases and mapping rules, but also allows agents to reason about relevant knowledge (focus) -- which are not known by the agents a priori -- in the context of a specific query. We present a use case scenario, some formalisations of the model proposed, and an initial implementation based on the BDI (Belief-Desire-Intention) agent model.