As growing numbers of real world applications involve imbalanced class distribution or unequal costs for mis- classification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of class distribution on 14 imbalanced data-sets by using statistical and graphical methods to address the important issue of understanding the effect of vary- ing levels of class distribution on bagging predictors. The experimental results demonstrate that bagging NB and MLP are insensitive to various levels of imbalanced class distribution.

Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Collaborative planning holds the promise of greatly improving performance on these tasks by leveraging the strengths of both humans and automated planners. However, this requires formalizing the problem domain and input, which must be done by hand, a priori, restricting its use in general real-world domains. We propose using a real-time crowd of workers to simultaneously solve the planning problem, formalize the domain, and train an automated system. As plans are developed, the system is able to learn the domain, and contribute larger segments of work.

Real-time transcription provides deaf and hard of hearing people visual access to spoken content, such as classroom instruction, and other live events. Currently, the only reliable source of real-time transcriptions are expensive, highly-trained experts who are able to keep up with speaking rates. Automatic speech recognition is cheaper but produces too many errors in realistic settings. We introduce a new approach in which partial captions from multiple non-experts are combined to produce a high-quality transcription in real-time. We demonstrate the potential of this approach with data collected from 20 non-expert captionists.

Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a formal model for planning in cooperative multiagent systems where agents operate with noisy sensors and actuators, and local information. Prevalent Dec-POMDP solution techniques have mostly been centralized and have assumed knowledge of the model. In real world scenarios, however, solving centrally may not be an option and model parameters maybe unknown. To address this, we propose a distributed, model-free algorithm for learning Dec-POMDP policies, in which agents take turns learning, with each agent not currently learning following a static policy. For agents that have not yet learned a policy, this static policy must be initialized. We propose a principled method for learning such initial policies through interaction with the environment. We show that by using such informed initial policies, our alternate learning algorithm can find near-optimal policies for two benchmark problems.

When an agent plans a sequence of actions, some unexpected events may occur during the execution of these actions. These unexpected events may prevent the agent to replan and achieve its goal. In this work, our purpose is to recover from plan execution failures by reasoning the causes of these faulties. We combine the TLPlan forward chaining temporal planner with the PROBCOG reasoning tool in order to handle failures. It is also quite important to decide whether the failure we are dealing with is permanent. We propose that inferring some properties of the failure source helps us handle failures and determine the failure types.

We present the concept of Bayesian Tool Affordances as a solution to estimate the suitable action for the given tool to realize the given novel effects to the robot. We define Tool affordances as the “awareness within robot about the different kind of effects it can create in the environment using a tool”. It incorporates understanding the bi-directional association of executed Action, functionally relevant features of the Tool and the resulting effects. We propose Bayesian leaning of Tool Affordances for prediction, inference and planning capabilities while dealing with uncertainty, redundancy and irrelevant information using limited learning samples. The estimation results are presented in this paper to validate the proposed concept of Bayesian Tool Affordances.

Multiagent Resource Allocation (MARA) distributes a set of resources among a set of intelligent agents in order to respect the preferences of the agents and to maximize some measure of global utility, which may include minimizing total costs or maximizing total return. We are interested in MARA solutions that provide optimal or close-to-optimal allocation of resources in terms of maximizing a global welfare function with low communication and computation cost, with respect to the priority of agents, and temporal dependencies between resources. We propose an MDP approach for resource planning in multiagent environments. Our approach formulates internal preference modeling and success of each individual agent as a single MDP and then to optimize global utility, we apply a market-based solution to coordinate these decentralized MDPs.

In case of a plan failure, plan-repair is a more promising solution than replanning from scratch. The effectiveness of plan-repair depends on knowledge of which plan action failed and why. Therefore, in this paper, we propose an Extended Spectrum Based Diagnosis approach that efficiently pinpoints failed actions. Unlike Model Based Diagnosis (MBD), it does not require the fault models and behavioral descriptions of actions. Our approach first computes the likelihood of an action being faulty and subsequently proposes optimal probe locations to refine the diagnosis. We also exploit knowledge of plan steps that are instances of the same plan operator to optimize the selection of the most informative diagnostic probes. In this paper, we only focus on diagnostic aspect of plan-repair process.

Details of our work can be found in the technical report, which is available at http://gqi.limewebs.com/aaaist12.pdf. In this paper, we propose a new operator for revising ABoxes in DL-Lite ontologies.

Active learning traditionally assumes that an oracle is capable of providing labeling information for each query instance. This paper formulates a new research problem which allows an oracle admit that he/she is incapable of labeling some query instances or simply answer "I don't know the label." We define a unified objectivefunction to ensure that each query instance submitted to the oracleis the one mostly needed for labeling and the oracle should also hasthe knowledge to label. Experiments based on different types of knowledge blind spot (KBS) models demonstrate the effectiveness of theproposed design.