An affective learning agent with Petri-net-based implementation

Traditional interactive evolutionary computing approaches are usually susceptible to limited searching ability and human’s strong subjectivity. In response, by extending a traditional Belief-Desire-Intention (BDI) structure, a kind of affective learning agent which can perform affective computing and learning activities in human-computer interaction environment is explicitly introduced. In solving human-computer interactive multi-objective decision-making problems whose objectives are usually far from well structured and quantified, this kind of agent may help reduce human’s subjective fatigue as well as make decisions more objective and scientific. Specifically, a conceptual model of the agent, affective learning-BDI (AL-BDI) agent, is proposed initially, along with corresponding functional modules to learn human’s affective preference. After that, a kind of high level Petri nets, colored Petri nets are employed to realize the components and scheduler of the AL-BDI agents. To exemplify applications of the approaches, test functions are suggested to case studies, giving rise to satisfied results and showing validity of the contribution.