Social networking has gained immense traction in many areas, including teaching and learning. Networking sites for teachers aim to facilitate teacher communication and information sharing, but fall short of their potential. In order to support more effective use of online resources and better communication among teachers, we develop a suite of new user modeling and recommendation capabilities within a middle school teacher networking site. We foster collaboration among novice and experienced teachers when they share similar interests, enabling new mentoring relationships, and promote the use of relevant educational resources. We illustrate our approach with an implemented system called PedConnect that analyzes user activities and presents intelligent suggestions for collaboration and resource use.

Humans have difficulty evaluating the effects of uncertainty on schedules. People often mitigate the effects of uncertainty by adding slack based on experience and non-stochastic analyses such as the critical path method (CPM). This is costly as it leads to longer than necessary schedules, and can be ineffective without a clear understanding of where slack is needed. COMPASS is an interactive real-time tool that analyzes schedule uncertainty for a stochastic task network. An important feature is that it concurrently calculates stochastic critical paths and critical tasks. COMPASS visualizes this information on top of a traditional Gantt view, giving users insight into how delays caused by uncertain durations propagate down the schedule. Evaluations with 10 users show that users can use COMPASS to answer a variety of questions about the possible evolutions of a schedule (e.g., what is the likelihood that all activities will complete before a given date?)

We propose an intelligent tutoring system that constructs a curriculum of hints and problems in order to teach a student skills with a rich dependency structure. We provide a template for building a multi-layered Dynamic Bayes Net to model this problem and describe how to learn the parameters of the model from data. Planning with the DBN then produces a teaching policy for the given domain. We test this end-to-end curriculum design system in two human-subject studies in the areas of finite field arithmetic and artificial language and show this method performs on par with hand-tuned expert policies.

In non-cooperative multi-agent situations, there cannot exist a globally optimal, yet opponent-independent learning algorithm. Regret-minimization over a set of strategies optimized for potential opponent models is proposed as a good framework for deciding how to behave in such situations. Using longer playing horizons and experts that learn as they play, the regret-minimization framework can be extended to overcome several shortcomings of earlier approaches to the problem of multi-agent learning.

In this paper we show how information contained in robust network codes can be used for passive inference of possible locations of link failures or losses in a network. For distributed randomized network coding, we bound the probability of being able to distinguish among a given set of failure events, and give some experimental results for one and two link failures in randomly generated networks. We also bound the required field size and complexity for designing a robust network code that distinguishes among a given set of failure events

In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algo- rithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited per- spective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and learn an effective policy.

We propose a new classification for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this classification, we review the optimality of ex- isting algorithms, including the case of interleague play. We propose an incremental improvement to the existing algorithms that seems to achieve average payoffs that are at least the Nash equilibrium payoffs in the long- run against fair opponents.