Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently also to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges: sparseness, ambiguity, noise, and inconsistency. We describe an approach to folksonomy learning based on relational clustering that addresses these challenges by exploiting structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr. We evaluate the learned folksonomy quantitatively by automatically comparing it to a reference taxonomy. Our empirical results suggest that the proposed framework, which addresses the challenges listed above, improves on existing folksonomy learning methods.

Wikis allow for collaborators to collect information about entities. In turn, such entity information can be used for AI tasks, such as information extraction. However, these collaborators are almost exclusively human users. Allowing arbitrary software agents to act as collaborators can greatly enrich a wiki since agents can contribute structured data to complement the human-contributed, unstructured-data. For instance, agents can import huge volumes of structured data about entities, enriching the pages, and agents can update wiki pages to reflect real-time information changes (e.g., win-loss records in sports). This paper describes an approach that allows for both arbitrary software agents and human users to collaborate. In particular, we address three key problems: agents updating the correct wiki pages, policies for agent updates, and sharing the schema across collaborators. Using our approach, we describe creating entity-focused wikis which include the ability to create dynamic categories of entities based on their wiki pages. These categories dynamically update their membership based upon real-world changes.

Collaborative filtering systems have been developed to manage information overload and improve discourse in online communities. In such systems, users rank content provided by other users on the validity or usefulness within their particular context. The goal is that "good" content will rise to prominence and "bad" content will fade into obscurity. These filtering mechanisms are not well-understood and have known weaknesses. For example, they depend on the presence of a large crowd to rate content, but such a crowd may not be present. Additionally, the community's decisions determine which voices will reach a large audience and which will be silenced, but it is not known if these decisions represent "the wisdom of crowds" or a "censoring mob." Our approach uses statistical machine learning to predict community ratings. By extracting features that replicate the community's verdict, we can better understand collaborative filtering, improve the way the community uses the ratings of their members, and design agents that augment community decision-making. Slashdot is an example of such a community where peers will rate each others' comments based on their relevance to the post. This work extracts a wide variety of features from the Slashdot metadata and posts' linguistic contents to identify features that can predict the community rating. We find that author reputation, use of pronouns, and author sentiment are salient. We achieve 76% accuracy predicting community ratings as good, neutral, or bad.

Since the expert systems movement of the 1980s and 1990s, - Joint inference between expert knowledge and general AI has had the dream of reproducing expert behavior in specialized Commonsense background knowledge; domains of knowledge, such as medicine or engineering, - Efficient inference, both forward and backward, of plausible by collecting knowledge from human experts. But assertions. the first generations of expert systems suffered from two problems -- first, the difficulty of knowledge engineering

Humans somehow manage to choose quite intelligently planner should fill in to produce a concrete plan that accomplishes the 20 trillion primitive motor commands that constitute a the goal as quickly as possible. It has long been thought that hierarchical structure in Planning at multiple levels of abstraction has long been a behavior is essential in managing this complexity. For instance, Shakey the exists at many levels, ranging from small (hundred-step?) robot used STRIPS for high-level task planning, then called motor programs for typing characters and saying phonemes out to separate low-level planning/control algorithms to execute up to large (billion-step?) actions such as writing an ICAPS each of the planned actions (Fikes and Nilsson 1971). This hard separation of levels, where a high-level plan is We believe that leveraging hierarchical structure will be chosen before considering low-level details, greatly simplifies equally important in achieving robust, efficient robotic behaviors. However, the resulting plans While your household robot probably won't get may be inefficient or even infeasible due to missed lowerlevel tenure anytime soon, even simple domestic tasks still have synergies and conflicts.

The impressive results of the 2007 DARPA Urban Challenge showed that fully autonomous vehicles are technologically feasible with current intelligent vehicle hardware. It is natural to ask how current transportation infrastructure can be improved when most vehicles are driven autonomously in the future. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that intersection control can be made more efficient than the traditional control mechanisms such as traffic signals and stop signs. In this paper, we extend the study by examining the relationship between the precision of cars' motion controllers and the efficiency of the intersection controller. We propose a planning-based motion controller that can reduce the chance that autonomous vehicles stop before intersections, and show that this controller can increase the efficiency of the intersection control mechanism.

The success of autonomous vehicles has made path planning in real, physically grounded environments an increasingly important problem. In environments where speed matters and vehicles must maneuver around obstructions, such as autonomous car navigation in hostile environments, the speed with which real vehicles can traverse a path is often dependent on the sharpness of the corners on the path as well as the length of path edges. We present an algorithm that incorporates the use of the turn angle through path nodes as a limiting factor for vehicle speed. Vehicle speed is then used in a time-weighting calculation for each edge. This allows the path planning algorithm to choose potentially longer paths, with less turns in order to minimize path traversal time. Results simulated in the Breve environment show that travel time can be reduced over the solution obtained using the Anytime D* Algorithm by approximately 10% for a vehicle that is speed limited based on turn rate.

Past educational robotics research has indicated that the use of simulators can increase students’ performance in introductory robotics programming courses. In this paper, we introduce a simulator for the iRobot Create that works on Windows PCs. It was developed to work with a Python robotics library and includes an Eclipse plugin, but can simulate any library that uses the serial Open Interface on the Create. The platform, library, and simulator are all easy to use and have been well-received initially by students.

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac- Man environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.

Many task execution techniques tend to repeatedly invoke motion planning algorithms in order to perform complex tasks. In order to accelerate the perform of such methods, we present a real-time global motion planner that utilizes the computational capabilities of current many-core GPUs (graphics processing units). Our approach is based on randomized sample-based planners and we describe highly parallel algorithms to generate samples, perform collision queries, nearest-neighbor computations, local planning and graph search to compute collision-free paths for rigid robots. Our approach can efficiently solve the single-query and multiquery versions of the planning problem and can obtain one to two orders of speedup over prior CPU-based global planning algorithms. The resulting GPU-based planning algorithm can also be used for real-time feedback for task execution in challenging scenarios.