Jason Induction of Logical Decison Trees (JILDT) is a library that provides a learning mechanism based on induction of logical decision trees to implement learner agents in Jason, the well-known Java-Based implementation of AgentSpeak(L).
Top-down Induction of Logical Decision Trees is an Inductive Logic Programming technique, adopted for learning in the context of rational agents. The first-order representation of Tilde is adequate to form training examples as sets of beliefs, e.g., the beliefs of the agent supporting the adoption of a plan as an intention; and the obtained hypothesis are useful for updating the plans and beliefs of the agents, i.e., a Logical Decision Tree expresses hypotheses about the successful or failed executions of the intentions.
Agents defined as instances of this class are able to learn about their reasons to adopt intentions based on their own experience. Two levels in the inductive process have been implemented: A Java-based level with computational performance in mind; and an AgentSpeak(L)-based level which opens the door for some particular forms of social learning. A set of internal actions and plans are provided for allowing the agents to autonomously perform inductive experiments. Implementation details can be reviewed in the published papers.
Download the latest version of JILDT(0.2) here.
Review the API documentation.
Technical Reports (In Spanish)
Intentional Learning: Performance Studies on Commitment. Advanced Computing and Intelligent Systems Master final report. University of Valencia. (2012)
Jason Induction of Logical Decision Trees (JILDT) : Una librería de aprendizaje y su aplicación al Compromiso. Artificial Inteligence Master thesis. University of Veracruz. (2010)
- C. A. González-Alarcón, Francisco Grimaldo, and A. Guerra-Hernández. Jason Intentional Learning: An Operational Semantics. In L. Correia, L.P. Reis and J. Cascalho editors, EPIA 2013, volume 8154 of Lecture Notes in Computer Science, pages 432 –443, Berlin Heidelberg, 2013. Springer-Verlag.
- A. Guerra-Hernández, C. A. González-Alarcón, and A. E. F. Seghrouchni. Jason Induction of Logical Decision Trees (Jildt): A learning library and its application to commitment. In 8th European Workshop in Multi-Agent Systems, EUMAS 2010, Paris, France, December 2010. LIPADE, Université Paris Descartes.
- A. Guerra-Hernández, C. A. González-Alarcón, and A. E. F. Seghrouchni. Jason Induction of Logical Decision Trees: A Learning Library and Its Application to Commitment. In V. Sidorov and A. Hernández editors, MICAI 2010, Part I, volume 6437 of Lecture Notes in Artificial Intelligence, pages 374 –385, Berlin Heidelberg, 2010. Springer-Verlag.
Carlos Alberto González Alarcón. University of Valencia.
Alejandro Guerra Hernandez. University of Veracruz.
Francisco Grimaldo Moreno. University of Valencia.