Fair cake-cutting is the division of a cake or resource among N users so that each user is content. Users may value a given piece of cake differently, and information about how a user values different parts of the cake can only be obtained by requesting users to “cut” pieces of the cake into specified ratios. Many distributed computing problems which involve resource sharing can be expressed as...

We study certain function algebras and their operator algebra completions on r-discrete abelian groupoids, the corresponding conditional expectations, maximal abelian subalgebras (masa) and eigen-functionals. We give a semidirect product decomposition for an abelian groupoid. This is done through a matched pair and leads to a C*-diagonal (for a special case). We use this decomposition to study ...

This paper revisits the continual-learning paradigm I described at the previous workshop in 1995. It presents a framework that formally merges ideas from reinforcement learning and inductive transfer, potentially broadening the scope of each. Most research in RL assumes a stationary (non-changing) world, while research in transfer primarily focuses on supervised learning. Combining the two appr...

This paper presents a novel application of answer set programming to concept learning in nonmonotonic logic programs. Given an extended logic program as a background theory, we introduce techniques for inducing new rules using answer sets of the program. The produced new rules explain positive/negative examples in the context of inductive logic programming. The result of this paper combines tec...

The selective transfer of task knowledge within the context of artificial neural networks is studied in the MTL learning framework, a modified version of the multiple task learning (MTL) method of functional transfer. MTL is a knowledge based inductive learning system that uses prior task knowledge to adjust its inductive bias. MTL employs a separate learning rate for each task output node. The...