90 citations · 273 across the 18 of their papers we have counts for
9 papers · 1 filter
Knowledge-Based Hierarchical POMDPs for Task Planning
Sergio A. Serrano, Elizabeth Santiago, Jose Martinez-Carranza +2
The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state. In robotics, this is particularly difficult because acti…
Causal Structure Learning: a Bayesian approach based on random graphs
Mauricio Gonzalez-Soto, Ivan R. Feliciano-Avelino, L. Enrique Sucar +1
A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existe…
Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models
Mauricio Gonzalez Soto, David Danks, Hugo J. Escalante Balderas +1
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available information is cons…
A Guiding Principle for Causal Decision Problems
M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
We define a Causal Decision Problem as a Decision Problem where the available actions, the family of uncertain events and the set of outcomes are related through the variables of a…
Playing against Nature: causal discovery for decision making under uncertainty
M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
We consider decision problems under uncertainty where the options available to a decision maker and the resulting outcome are related through a causal mechanism which is unknown to…
Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks
Luis Enrique Sucar, Duncan F. Gillies
Bayesian networks have been used extensively in diagnostic tasks such as medicine, where they represent the dependency relations between a set of symptoms and a set of diseases. A…