Dr. Francisco J. Cantú Ortiz
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Research interests


My research interests are in the areas of knowledge theory, learning and probabilistic reasoning including:
  • Probabilistic reasoning in logical frameworks
  • Bayeasian reasoning and learning for knowledge tasks (prediction, diagnosis, design, discovery in databases, etc.)
  • Knowkedge based-systems: acquisition, logic representation, use
  • The philosophy and theory of knowledge
We (my students and I) are working in the following areas:
  • The use of formal methods in automating reasoning and representing knowledge. The automation of mathematical induction and its application to system verification. The verification of hardware and software systems using proof planning and higher-order logic in the context of theorem proving. I am doing work in the use of planning techniques and meta-level reasoning for the automatic verification of industrial-strenght circuits.
  • The use of Bayesian theory to handle uncertainty in decision making and knowledge discovery. In particular I am investigating the connection between Bayesian reasoning and first-order logic representations such as Horn Logic. The Independence Choice Logic (ICL) is a framework developed by David Poole which combines Horn Logic and Bayesian reasoning for decision making. We are working on extensions to ICL to apply it to the diagnosis of industrial.strength problems such as electrical power networks, and to the control of dynamic systems, such as industrial chemical processes. We are trying to imbed Dynamic Bayesian Networks within ICL to reason about time in industrial process control. We also have an interest on Bayesian and reinforcement learning in multiagent systems for user profiling.
  • The connection between symbols, data, information, and knowledge, as well as the inference procedures inherently attached to them. Information and knowledge are treated as mathematical relationships among data and information items. We are interested in the inference mechanisms attached to knowledge representation techniques. In particular, we are interested in the CommonKADS method for knowledge engineering and management. Our  work has been inspired by the Heuristic Classification method propose by William Clancey in the early 80s
  • The use of artificial intelligence technologies for industrial applications in the areas of optimization, planning, user modelling, verification and decision support.  This includes the integration of AI technologies such as expert systems, decision-support systems, machine learning, automated reasoning, and multiagent systems. Important applications are arising in the areas of electronic commerce, electronic business, and knowledge organizations. The aim is to support the development of intelligent, knowledge-based organizations through the use of AI technologies.


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