Design of an AI-Driven Communication Framework for Conflict Mediation in Multi-Agent and Multi-Species Environments

Abstract

Communication plays a basic role of helping to settle down a disagreement and promoting cooperation between intelligent beings within human and non-human spheres. The study introduces a new AI-based communication scheme intended to be applied to conflict mediation in multi-agent and multi-species systems, and applicable in combining advanced computation models of language, emotion, and behaviour. The system suggested here utilises agent-based modelling to generate the dynamics of negotiation between heterogeneous agents, that is, human, artificial, and alien analogue intelligences, of different cognitive and affective qualities. Based on Natural Language Processing (NLP) to understand the semantics of the message, emotion detection algorithms to understand our affective condition, and adaptive dialogue management to modify responses in real time, the framework allows adapting conversations to the context and reacting sentimentally. A hybrid system that uses transformer-based dialogue systems and reinforcing learning is better because communication strategy can be optimised in a manner that cooperative equilibrium can be achieved in the simulated conflicts. Experimental simulated experiments indicate that sentiment-cognizant dialogue control allows de-escalation processes to be more efficient, as well as raises cooperation rates by about 45 percent in comparison to rule-based and emotion-neutral systems. The findings imply that emotional and linguistic adaptability play a critical role on determining the development of trust, empathy modelling, and success when negotiating in a heterogeneous agent context. In addition to theoretical implications, there are promising applications to human-robot collaboration, autonomous system coordination, training cross-cultural 
diplomacy, and interspecies communication modelling because misunderstanding or emotional incompatibility can result in system-wide conflict. This study combines cognitive model, affective computing and reinforcement learning to come up with socially intelligent artificial intelligence systems that understand and respond to the emotional and communicative subtleties of diverse beings and eventually moves the frontier of adjudictive, empathetic and adaptive artificial intelligence in complex ecosystems involving many different entities.