# Incorporating social practices in BDI agent systems

## TL;DR

This paper proposes a novel approach to enhance BDI agent systems by integrating social practices, enabling flexible, scalable, and robust human-agent interactions in dynamic environments.

## Contribution

It introduces a method to incorporate social practices into BDI agents through extended deliberation cycles and a meta-deliberation plan in Jason.

## Key findings

- Implemented in Jason with a meta-deliberation plan
- Supports dynamic, flexible interaction protocols
- Demonstrated in a realistic scenario

## Abstract

When agents interact with humans, either through embodied agents or because they are embedded in a robot, it would be easy if they could use fixed interaction protocols as they do with other agents. However, people do not keep fixed protocols in their day-to-day interactions and the environments are often dynamic, making it impossible to use fixed protocols. Deliberating about interactions from fundamentals is not very scalable either, because in that case all possible reactions of a user have to be considered in the plans. In this paper we argue that social practices can be used as an inspiration for designing flexible and scalable interaction mechanisms that are also robust. However, using social practices requires extending the traditional BDI deliberation cycle to monitor landmark states and perform expected actions by leveraging existing plans. We define and implement this mechanism in Jason using a periodically run meta-deliberation plan, supported by a metainterpreter, and illustrate its use in a realistic scenario.