130,098 research outputs found
Searching for a Solution to Program Verification=Equation Solving in CCS
International audienceUnder non-exponential discounting, we develop a dynamic theory for stopping problems in continuous time. Our framework covers discount functions that induce decreasing impatience. Due to the inherent time inconsistency, we look for equilibrium stopping policies, formulated as fixed points of an operator. Under appropriate conditions, fixed-point iterations converge to equilibrium stopping policies. This iterative approach corresponds to the hierarchy of strategic reasoning in game theory and provides âagent-specificâ results: it assigns one specific equilibrium stopping policy to each agent according to her initial behavior. In particular, it leads to a precise mathematical connection between the naive behavior and the sophisticated one. Our theory is illustrated in a real options model
Combined Reinforcement Learning via Abstract Representations
In the quest for efficient and robust reinforcement learning methods, both
model-free and model-based approaches offer advantages. In this paper we
propose a new way of explicitly bridging both approaches via a shared
low-dimensional learned encoding of the environment, meant to capture
summarizing abstractions. We show that the modularity brought by this approach
leads to good generalization while being computationally efficient, with
planning happening in a smaller latent state space. In addition, this approach
recovers a sufficient low-dimensional representation of the environment, which
opens up new strategies for interpretable AI, exploration and transfer
learning.Comment: Accepted to the Thirty-Third AAAI Conference On Artificial
Intelligence, 201
Problem-Solving Knowledge Mining from Usersâ\ud Actions in an Intelligent Tutoring System
In an intelligent tutoring system (ITS), the domain expert should provide\ud
relevant domain knowledge to the tutor so that it will be able to guide the\ud
learner during problem solving. However, in several domains, this knowledge is\ud
not predetermined and should be captured or learned from expert users as well as\ud
intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud
techniques can help to build this domain intelligence in ITS. This paper proposes\ud
a framework to capture problem-solving knowledge using a promising approach\ud
of data and knowledge discovery based on a combination of sequential pattern\ud
mining and association rules discovery techniques. The framework has been implemented\ud
and is used to discover new meta knowledge and rules in a given domain\ud
which then extend domain knowledge and serve as problem space allowing\ud
the intelligent tutoring system to guide learners in problem-solving situations.\ud
Preliminary experiments have been conducted using the framework as an alternative\ud
to a path-planning problem solver in CanadarmTutor
- âŠ