We introduce a laboratory text on environmental and societal sustainability applications that can be a supplemental resource for any undergraduate AI course. The lab text, entitled Artificial Intelligence for Computational Sustainability: A Lab Companion, is brand new and incomplete; freely available through Wikibooks; and open to community additions of projects, assignments, and explanatory material on AI for sustainability. The project adds to existing educational efforts of the computational sustainability community, encouraging the flow of knowledge from research to education and public outreach. Besides summarizing the laboratory book, this paper touches on its implications for integration of research and education, for communicating science to the public, and other broader impacts.

In recent years, major research advances have taken place in the intersection of computer science and economics, but this material has so far been taught primarily at the graduate level. This paper describes a novel semester-long undergraduate-level course in the intersection of computer science and economics at Duke University, titled “CPS 173: Computational Microeconomics.”

We present a study of how humans represent space when solving Tower Defense puzzles, a complex spatial reasoning task requiring the subject to protect locations by arranging a set of defense towers at strategic positions. We have discovered that the representation humans use is significantly more complex than what is needed to describe the spatial situation. Strategy and spatial representations are tightly intertwined with spatial representations forgoing objective, atomically-defined spatial features for context-sensitive, goal-oriented spatial affordances. Spatial relationships exist not only between objects but between an object’s properties, second-order properties, joint spatial properties and temporal properties.

This research proposes the use of imitation based learning to build collaborative strategies for a team of agents. Imitation based learning involves learning from an expert by observing her demonstrating a task and then replicating it. This mechanism makes it extremely easy for a knowledge engineer to transfer knowledge to a software agent via human demonstrations. This research aims to apply imitation to learn not only the strategy of an individual agent but also the collaborative strategy of a team of agents to achieve a common goal. The effectiveness of the proposed methodology is being assessed in the domain of RoboCup Soccer Simulation 3D which is a promising platform to address many of the complex real-world problems and offers a truly dynamic, stochastic, and partially-observable environment.

Much work in learning from demonstration has focused on learning simple tasks from structured demonstrations that have a well-defined beginning and end. As we attempt to scale robot learning to increasingly complex tasks, it becomes intractable to learn task policies monolithically. Furthermore, it is desirable to be able to learn from natural, unstructured demonstrations, which are unsegmented, possibly incomplete, and may come from different tasks. We propose a three-part approach to designing a natural, scalable system that allows a robot to learn tasks of increasing complexity by automatically building and refining a library of skills over time. First, we describe a Bayesian nonparametric model that can segment unstructured demonstrations into appropriate numbers of component skills and recognize repeated skills across demonstrations and tasks. These skills can then be parameterized and generalized to new situations. Second, we propose to create a system that allows the user to provide unstructured corrections and feedback to the robot, without requiring any knowledge of the robot's underlying representation of the task or its component skills. Third, we propose to infer the user's intentions for each segmented skill and autonomously improve these skills using reinforcement learning. This approach will be applied to learn and generalize complex, multi-step tasks that are beyond the reach of current LfD methods, using the PR2 mobile manipulator as a testing platform.

Group Recommendation Systems (GRS's) assist groups when trying to reach a joint decision. I use probabilistic data and apply voting theory to GRS’s in order to minimize user interaction and output an approximate or definite “winner item

In a dynamic environment, an intelligent agent must consider unexpected changes to the world and plan for them. We aim to address this key issue by building more robust artificial agents through the generalization of plan representations. Our research focuses on the process of generalizing various plan forms and the development of a compact representation which embodies a generalized plan as a policy. Our techniques allow an agent to execute efficiently in an online setting. We have, to date, demonstrated our approach for sequential and partial order plans and are pursuing similar techniques for representations such as Hierarchical Task Networks and GOLOG programs

Prior work done in learning by instruction (Huffman and Laird, 1995) Learning by interacting with humans is a powerful learning demonstrated learning systems that focus on agent-initiated paradigm. In a complex world learning through self-directed interaction, where instruction is directed by impasses arising experience alone can be slow, requiring repeated interactions in a Soar agent. They noted that instructor-initiated interaction with the environment. Learning from human-agent interaction is difficult to support because of the likely interruption can reduce the complexity of the learning task by reducing of agent's reasoning.

Multiagent resource allocation under uncertainty raises various computational challenges in terms of efficiency such as intractability, communication cost, and preference representation. To date most approaches do not provide efficient solutions for dynamic environments where temporal constraints pose particular challenges. We propose two techniques to cope with such settings: auctions to allocate fairly according to preferences, and MDPs to address stochasticity. This research seeks to determine the ideal combination between the two methods to handle wide range of allocation problems with reduced computation and communication cost between agents.

Current Coreference Resolution systems utilize a broad range of general knowledge features to make resolutions in a general setting. These approaches ignore coreference knowledge found in domain specific collections and how coreferent entities interact in different domains. This research addresses these issues by developing knowledge bases of coreference characteristics drawn from annotated and unannotated domain texts and utilizing lexical and discourse information to improve resolution.