In a previous paper, Liu argued for the importance of establishing a precise theoretical foundation for program debugging from first principles. In this paper, we present a first ...
Understanding conceptual change is an important problem in modeling human cognition and in making integrated AI systems that can learn autonomously. This paper describes a model o...
We consider the problem of incorporating end-user advice into reinforcement learning (RL). In our setting, the learner alternates between practicing, where learning is based on ac...
Kshitij Judah, Saikat Roy, Alan Fern, Thomas G. Di...
Evolutionary trees of species can be reconstructed by pairwise comparison of their entire genomes. Such a comparison can be quantified by determining the number of events that cha...
Lifted inference, handling whole sets of indistinguishable objects together, is critical to the effective application of probabilistic relational models to realistic real world ta...
Kristian Kersting, Youssef El Massaoudi, Fabian Ha...
g Without a Heuristic: Efficient Use of Abstraction Bradford Larsen, Ethan Burns, Wheeler Ruml Department of Computer Science University of New Hampshire Durham, NH 03824 USA blars...
Bradford John Larsen, Ethan Burns, Wheeler Ruml, R...
Software developers increasingly rely on information from the Web, such as documents or code examples on Application Programming Interfaces (APIs), to facilitate their development...
Jinhan Kim, Sanghoon Lee, Seung-won Hwang, Sunghun...
We design minimal temporal description logics that are capable of expressing various aspects of temporal conceptual data models and investigate their computational complexity. We ...
Alessandro Artale, Roman Kontchakov, Vladislav Ryz...
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...