Brazil

Bed allocation in hospitals is a critical and important problem, and it has become even more important since last year because of the COVID-19 pandemic. In this paper, we present an approach based on intelligent-agent technologies to assist hospital staff in charge of bed allocation. As part of this work, we developed a web-based simulation of a hospital bed allocation system integrated with a chatbot for interaction with the user. As a core component in our approach, an intelligent agent uses the feedback of a plan validator to check if there are any flaws in a user-made allocation, communicating any detected problems to the user using natural language through the chatbot. Thus, our resulting application not only validates bed allocation plans but also interacts with hospital professionals using natural language communication, including giving explainable suggestions of better alternative allocations. We evaluated our approach with professionals responsible for bed allocation in two local hospitals and a doctor who provides consultancy to another local hospital. The version of the system reported in this paper addresses all the suggestions made by the specialists who evaluated its previous version.

BRACIS 2021

A Conversational Agent to Support Hospital Bed Allocation

intelligent agent

hospital bed allocation

chatbot

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

technical paper

Task-oriented dialogue systems are very important due to their wide range of applications. In particular, those tasks that involve multiple domains have gained increasing attention in recent years, as the actual tasks performed by virtual assistants often span multiple domains. In this work, we propose the Divide-and-Conquer Distributed Architecture with Slot Sharing Mechanism (DCDA-S2M) system, which includes not only a distributed system aimed at managing dialogues through the pipeline architecture, but also a slot sharing mechanism that allows the system to obtain information during a conversation in one domain and reuse it in another domain, in order to avoid redundant interactions and make the dialogue more efficient. Results show that the distributed architecture outperforms the centralized one by 21.51% and that the slot sharing mechanism improves the system performance both in the success rate and in the number of turns during the dialogue, demonstrating that it can prevent the agent from requesting redundant information.

Slot Sharing Mechanism in Multi-Domain Dialogue Systems

The recent progress in Reinforcement Learning applications to Resource Management presents MDPs without a deeper analysis of the impacts of design decisions on agent performance. In this paper, we compare and contrast four different MDP formulations, discussing their computational requirements and impacts on agent performance by means of an empirical analysis. We conclude by showing that, when used with Multi-Layer Perceptrons, image-like formulations are more helpful when shorter jobs dominate in the data set, while compact formulations are more helpful when longer jobs are used, and when transfer learning is desired as well.

On the impact of MDP design for Reinforcement Learning agents in Resource Management

The coronavirus disease (COVID-19) pandemic has brought significant challenges worldwide through the consequences of increasing demand for the Intensive Care Unit (ICU) resources. This work presents the Multi-Agent System for Glycemic Control (MAS4GC) to assist health professionals leading with critical patients in the ICU. More specifically, the MAS4GC manages patients’ blood glucose through glycemic predictions, treatment, and monitoring recommendations to health professionals. Prediction models are applied to monitor patients’ blood glucose allowing health professionals to carry out preventive treatments. The glycemic control is included in the FAST HUG mnemonic to remember the key issues in the supportive care of critically ill patients. The MAS4GC methodological development process is presented with Tropos modeling, architectural design, and implementation with the PADE framework. Agents’ inference mechanisms are based on production rules defined by intensive care physician specialists applying their knowledge to indicate treatments for patients. Two experiments using patients with synthetic data were conducted to evaluate the results of the MAS4GC: (1) the prediction model achieved 90\% accuracy in blood glucose predictions for the next 4 hours, (2) 84\% similarity of treatment recommendations compared to a human specialist, and 78\% in recommendations for monitoring glycemic of critical patients.

MAS4GC: Multi-Agent System for Glycemic Control of Intensive Care Unit Patients

Stochastic Value Gradient (SVG) methods underlie many recent achievements of model-based Reinforcement Learning agents in continuous state-action spaces. Despite their practical significance, many algorithm design choices still lack rigorous theoretical or empirical justification. In this work, we analyze one such design choice: the gradient estimator formula. We conduct our analysis on randomized Linear Quadratic Gaussian environments, allowing us to empirically assess gradient estimation quality relative to the actual SVG. Our results justify a widely used gradient estimator by showing it induces a favorable bias-variance tradeoff, which could explain the lower sample complexity of recent SVG methods.

Gradient Estimation in Model-Based Reinforcement Learning: a Study on Linear Quadratic Environments

Argumentation-based persuasive negotiation is a form of negotiation dialogue in which agents, with different interests and goals, exchange proposals that are supported by rhetorical arguments such as threats, rewards, or appeals. Besides rhetorical arguments, additional kinds of illocutions may also be exchanged during the dialogue, for instance, agents may ask for explanations, give explanations, or attack (or contradict) previous arguments. This paper presents a formal protocol for argumentation-based persuasive negotiation dialogues in which a proponent agent tries to persuade his opponent to perform a given action and the opponent tries to maintain his position. The protocol is modelled as a dialogue game (i.e. the interactions between the proponent and the opponent are governed by a set of rules) and the outcome of the dialogue is determined by applying an argumentation semantics. We prove the soundness and completeness of our proposal and illustrate the proposed protocol by using an example.

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A Conversational Agent to Support Hospital Bed Allocation

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