Taking decisions in a Hybrid Conversational AI architecture using Influence Diagrams
Authors: Roberto Basile Giannini, Antonio Origlia, Maria Di Maro (Università di Napoli Federico II, 2024)
Topic: Chatbots and Dialogue Systems

Abstract
This paper explores the application of the Influence Diagrams model for decision-making in the context of conversational agents. The system consists of a Conversational Recommender System (CoRS), in which the decision-making module is separate from the language generation module. It provides the capability to evolve a belief based on user responses, which in turn influences the decisions made by the conversational agent. The proposed system is based on a pre-existing CoRS that relies on Bayesian Networks informing a separate decision process. The introduction of Influence Diagrams aims to integrate both Bayesian inference and the dialogue move selection phase into a single model, thereby generalising the decision-making process. To test the effectiveness and plausibility of the dialogues generated by the developed CoRS, a dialogue simulator was created and the simulated interactions were evaluated by a pool of human judges.

Introduction
In recent years, the success of neural networks has generated significant enthusiasm among professionals in the field of artificial intelligence as well as the general public. Various applications, such as speech recognition, computer vision and even interactive conversational models like ChatGPT, have increasingly engaged users, inevitably shaping their perception of AI. This perception can have various implications, even within the scientific community. Attributing human-level intelligence to the tasks currently accomplished by neural networks is questionable, as these tasks barely rise to the level of abilities possessed by many animals AUTHOR. Neural-based approaches to artificial intelligence have been criticised because of the limitations that are intrinsic to purely associative methods. One notable analysis of the problems that come when considering linguistic material generated without a real understanding of the meaning of what is being said is found in AUTHOR, which highlights that, because of the way it is generated, content produced by GPT models adheres to at least one formal definition of bullshit. The fundamental problem with these models is that, while they are trained to capture surface aspects of communication, they are never exposed to the reasons why language is produced. When they output the most probable continuation of the provided prompt, they leave entirely to the human reader the task of interpreting what the produced output might have meant. From a linguistics point of view, within the framework of Austin's speech act theory AUTHOR ``saying something" equals ``doing something"; the act of producing a sentence (locutive act) is fuelled by an intention (illocutive act) that produces changes in the world (perlocutive act). This classic view of the act of speaking highlights that conversation is a form of intervention in the world: it is put in action to alter in some way the conversational context. This same position is also found in the recent literature about the role of causality in artificial intelligence. Judea Pearl's Ladder of Causation AUTHOR puts intervention capabilities on the second level of the ladder, characterised by the verb doing, as in Austin's seminal work. In this work, machine learning capabilities are limited to the first step of the Ladder, concerned with observational capabilities, leaving interventional ones out. From this perspective, a conversational agent that produces language motivated by the achievement of a goal, thus modelling a raison d'exprimer, is an agent capable of using language with interventional purposes, which can be placed on the second step of the Ladder of Causation. A tool that aims to define conversational agents according to this philosophy is the Framework for Advanced Natural Tools and Applications with Social Interactive Agents (FANTASIA) AUTHOR, an Unreal Engine plugin designed to develop embodied conversational agents. Built upon the functionalities offered by the tool, the FANTASIA Interaction Model follows these main principles: Behaviour Trees (BT) AUTHOR are used to organise and prioritise dialogue moves; Graph Databases (i.e., Neo4j AUTHOR) are used for knowledge representation and dialogue state tracking; Probabilistic Graphical Models (PGM) are used for decision making; LLMs are used to verbalise the decisions taken by PGMs. The latest results obtained using FANTASIA, presented in AUTHOR, used a decision system based on Bayesian Networks to estimate probability distributions over ratings for users of a movie recommender system. The decision about the dialogue move was taken by a rule-based system taking into account these estimates. In this work, we further develop the approach by generalising the decision process using a single model, an Influence Diagram (ID) AUTHOR. IDs represent an extension of BNs AUTHOR since, in addition to probabilistic nodes, they also contain: \item {Decision nodes, which represent decision points for the agent and which may be multiple within the model. \item {Utility nodes, which represent utility (or cost) factors and which will drive the agent’s decisions, since the objective will be to maximise the utility of the model. Consequently, in addition to the modelling of probabilistic inference problems, the use of IDs also enables the modelling and solving of decision-making problems, in accordance with the criterion of maximum expected utility. In this way, the ID encapsulates both the Bayesian inference and the decision phase in a single, more flexible and elegant model.

Conclusion
The results obtained indicate that the implementation of a knowledge graph exploration strategy based on the ID is more effective than a random strategy. This conclusion is further supported by objective measures, including the system’s entropy, which decreases as the system accumulates information during the dialogue before making a recommendation. It is therefore possible to generalise within an ID a decision-making process that, in the original system, was implemented by a module external to the probabilistic model. The results achieved in this case were lower than the ones of the original system, but this was expected as only part of the original strategy was replicated. Future work will cover the implementation of the missing functionalities and the deployment of the system in the Unreal Engine, as the technology to implement IDs has been integrated in the FANTASIA plugin. We will also investigate the possibility of integrating the argument selection process in the ID to fully support Argumentation Based Dialogue. This work is supported by the Supporting Patients with Embodied Conversational Interfaces and Argumentative Language (SPECIAL) project, funded by the University of Naples on the "Fondi per la Ricerca di Ateneo" (FRA) program (CUP: E65F22000050001)