Neural-symbolic BDI-Agent as a Multi-Context System: A case study with negotiating agent

Abstract

Intelligent systems are already being deployed in several fields (e.g., medicine, education, automation, or legal), providing relevant tools to assist in our daily tasks. It is claimed that the next step of AI is the integration of connectionist and symbolic methods. Connectionist methods embody knowledge by assigning numerical conductivities or weights to the connections inside a network of nodes. Symbolic methods work by carrying on a sequence of logic-like reasoning steps over a set of symbols consisting of language-like representations. Combining the strengths of connectionist and symbolic methods can allow the development of robust intelligent systems. We propose a neural-symbolic BDI-agent based Multi-Context Systems (MCS) model to integrate these two methods. Neural-symbolic area explores the effective integration of connectionist and symbolic methods, more precisely, learning and reasoning. MCSs allow the representation of information exchange among heterogeneous sources. BDI-agents offer robust and flexible behavior, rapid and modular development, intelligibility, and verifiability. In our work, MCSs represent the symbolic method and Neural Networks the connectionist method. We present a case study of how the proposed agent can be implemented using the Sigon framework. Sigon facilitates the development of MCS agents using a programming language-like paradigm. In this case study, we employed a trained MultiLayer Perceptron (MLP) neural network, enabling us to mitigate the necessity of modeling some hand-crafted rules in symbolic systems. We performed two experiments, in which the main goal was to analyze the impact of using a neural network during the agent’s decision-making. In the first experiment, we compare the proposed agent with 136 negotiating agents participating in the Automated Negotiating Agents Competition (ANAC) available on the GENIUS framework. In the second experiment, we compare our work with one similar to ours. The results show that our agent can adapt and achieve high utility functions for different situations. However, it is necessary to consider the computational cost of using the NN’s output in every reasoning cycle. The contributions of this paper are: (i) - integration of connectionist methods as part of the agent’s decision-making, enabling the development of agents in a modular and flexible fashion; (ii) - custom sensors to handle different data types; (iii) - practical implementation of the proposed integration method; and (iv) - experiments of the proposed integration method, focusing on mitigating the necessity of hard-coded and hand-crafted rules.