For ontologies represented as Description Logic Tboxes, optimised DL reasoners are able to detect logical errors, but there is comparatively limited support for resolving such pro...
Thomas Andreas Meyer, Kevin Lee, Richard Booth, Je...
Providing agents with strategies that will be robust against deviations by coalitions is central to the design of multi-agent agents. However, such strategies, captured by the not...
We present a new efficient algorithm for obtaining utilitarian optimal solutions to Disjunctive Temporal Problems with Preferences (DTPPs). The previous state-of-the-art system ac...
The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-...
NSF and NASA sponsored a workshop to discuss harvesting solar power in space. One solution considered was the use of a swarm of robots to form a solar reflector. How can these rob...
A long-standing goal of AI is the development of intelligent workstation-based personal agents to assist users in their daily lives. A key impediment to this goal is the unrealist...
Tom M. Mitchell, Sophie H. Wang, Yifen Huang, Adam...
This paper presents multi-conditional learning (MCL), a training criterion based on a product of multiple conditional likelihoods. When combining the traditional conditional proba...
Andrew McCallum, Chris Pal, Gregory Druck, Xuerui ...
This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto...
Few temporal planners handle both concurrency and uncertain durations, but these features commonly co-occur in realworld domains. In this paper, we discuss the challenges caused b...