MUAHAH: Taking the Most out of Simple Conversational Agents

Authors Leonor Llansol , João Santos , Luís Duarte , José Santos , Mariana Gaspar , Ana Alves , Hugo Gonçalo Oliveira , Luísa Coheur

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Author Details

Leonor Llansol
  • INESC-ID & Instituto Superior Técnico, Porto, Portugal
João Santos
  • INESC-ID & Instituto Superior Técnico, Porto, Portugal
Luís Duarte
  • CISUC, DEI, Universidade de Coimbra, Portugal
José Santos
  • CISUC, DEI, Universidade de Coimbra, Portugal
Mariana Gaspar
  • INESC-ID & Instituto Superior Técnico, Porto, Portugal
Ana Alves
  • CISUC & Instituto Politécnico de Coimbra, Portugal
Hugo Gonçalo Oliveira
  • CISUC, DEI, Universidade de Coimbra, Portugal
Luísa Coheur
  • INESC-ID & Instituto Superior Técnico, Porto, Portugal

Cite AsGet BibTex

Leonor Llansol, João Santos, Luís Duarte, José Santos, Mariana Gaspar, Ana Alves, Hugo Gonçalo Oliveira, and Luísa Coheur. MUAHAH: Taking the Most out of Simple Conversational Agents. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 7:1-7:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Dialog engines based on multi-agent architectures usually select a single agent, deemed to be the most suitable for a given scenario or for responding to a specific request, and disregard the answers from all of the other available agents. In this work, we present a multi-agent plug-and-play architecture that: (i) enables the integration of different agents; (ii) includes a decision maker module, responsible for selecting a suitable answer out of the responses of different agents. As usual, a single agent can be chosen to provide the final answer, but the latter can also be obtained from the responses of several agents, according to a voting scheme. We also describe three case studies in which we test several agents and decision making strategies; and show how new agents and a new decision strategy can be easily plugged in and take advantage of this platform in different ways. Experimentation also confirms that considering several agents contributes to better responses.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
  • Computing methodologies → Multi-agent systems
  • Computing methodologies → Artificial intelligence
  • Information systems → Information retrieval
  • Dialog systems
  • question answering
  • information retrieval
  • multi-agent


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