Many important computational problems that arise in various areas of AI are intractable. Nevertheless, AI research was very successful in developing and implementing heuristic solvers that work well on realworld instances. An important component of virtually every solver is a powerful polynomial-time preprocessing procedure that reduces the problem input. For instance, preprocessing techniques for the propositional satisfiability problem are based on Boolean Constraint Propagation (see, e.g., Eén and Biere, 2005), CSP solvers make use of various local consistency algorithms that filter the domains of variables (see, e.g., Bessière, 2006); similar preprocessing methods are used by solvers for Nonmonotonic and Bayesian reasoning problems (see, e.g., Gebser et al., 2008, Bolt and van der Gaag, 2006, respectively). Until recently, no provable performance guarantees for polynomial-time preprocessing methods have been obtained, and so preprocessing was only subject of empirical studies. A possible reason for the lack of theoretical results is a certain inadequacy of the P vs NP framework for such an analysis: if we could reduce in polynomial time an instance of an NPhard problem just by one bit, then we can solve the entire problem in polynomial time by repeating the reduction step a polynomial number of times, and P NP follows. With the advent of parameterized complexity (Downey, Fellows, and Stege, 1999), a new theoretical framework became available that provides suitable tools to analyze the power of preprocessing. Parameterized complexity considers a problem in a two-dimensional setting, where in addition to the input size n, a problem parameter k is taken into consideration.

Frustration has been generally viewed in a negative light and its potential role in learning neglected. We propose a new approach to intrinsically motivated learning where frustration is a key factor that allows to dynamically balance exploration and exploitation. Moreover, based on the result obtained from our experiment with older infants, we propose that a temporary decrease in learning from negative feedback can also be beneficial in fine-tuning a newly learned behavior. We suggest that this temporal indifference to the outcome of an action may be related to the sense of control, and results from the state of elation, that is the experience of overcoming a very difficult task after prolonged frustration. Our preliminary simulation results serve as a proof-of-concept for our approach.

The social world that most of us navigate effortlessly can prove to be a perplexing and disconcerting place for individuals with autism. Currently there are no models to assist non-expert authors as they create customized social script-based instructional modules for a particular child. We describe an approach to using human computation to develop complex models of social scripts for a plethora of complex and interesting social scenarios, possible obstacles that may arise in those scenarios, and potential solutions to those obstacles. Human input is the natural way to build these models, and in so doing create valuable assistance for those trying to navigate the intricacies of a social life.

The fact that many AI planning approaches are still based on too simplifying assumptions makes it often hard to apply these approaches to real-world robotics. In particular, it is in many cases difficult to generate a complete plan in advance, because not all information is available at the beginning of the planning process. We briefly present the continual planning system ACogPlan and a preliminary test case that demonstrates how the planning system can enable mobile robots to continually plan and execute activities in an open-ended domain.

Bayesian networks are a theoretically well-founded approach to represent large multi-variate probability distributions, and have proven useful in a broad range of applications. While several software tools for visualizing and editing Bayesian networks exist, they have important weaknesses when it comes to enabling users to clearly understand and compare conditional probability tables in the context of network topology, especially in large-scale networks. This paper describes a system for improving the ability for computers to work with people to develop intelligent systems through the construction of high-performing Bayesian networks. We describe NetEx, a tool developed as a Cytoscape plug-in, which allows a user to visually inspect and compare details concerning multiple nodes in a Bayesian network while maintaining awareness of their network context. It uses a "thought bubble line" to connect nodes in a graph representation and their internal information at the side of the graph. The tool seeks to improve the ability of experts to analyze and debug large Bayesian network models, and to help people to understand how alternative algorithms and Bayesian networks operate, providing insights into how to improve them.

Many practitioners currently use rules of thumb to price tasks on online labor markets. Incorrect pricing leads to task starvation or inefficient use of capital. Formal pricing policies can address these challenges. In this paper we argue that a pricing policy can be based on the trade-off between price and desired completion time.We show how this duality can lead to a better pricing policy for tasks in online labor markets. This paper makes three contributions. First, we devise an algorithm for job pricing using a survival analysis model. We then show that worker arrivals can be modeled as a non-homogeneous Poisson Process (NHPP). Finally using NHPP for worker arrivals and discrete choice models we present an abstract mathematical model that captures the dynamics of the market when full market information is presented to the task requester. This model can be used to predict completion times and pricing policies for both public and private crowds.

The major limitation to standard classification techniques is that the classifiers have to be trained on objects for which the ground truth, ACT-R contains a robust declarative memory module, which in terms of either a pre-assigned label or an error signal, is stores information as "chunks." A chunk in ACT-R may contain known. This limitation prevents the classifiers from dynamically any number of slots and values for those slots; slot values developing their own categories of classification based may be other chunks, numbers, strings, lists, or generally on information obtained from the environment. Previous attempts any data type allowed in Lisp (the base language for to overcome these limitations have been based on ACT-R). Retrieval from declarative memory is handled by a classical machine learning algorithms (Modayil and Kuipers request to the retrieval module; the request specifies the conditions 2007) (Kuipers et al. 2006). Here we present an alternative to be met in order for a chunk to be retrieved from approach to this problem, and develop the beginnings of declarative memory, and the module either returns a chunk a framework within which a classifier can evolve its own matching those specifications or generates a failure signal if representations based on dynamical information from the a retrieval cannot be made.

Continual learning is the unending process of learning new things on top of what has already been learned (Ring, 1994).Temporal Transition Hierarchies (TTHs) were developed to allow prediction of Markov-k sequences in a way that was consistent with the needs of a continual-learning agent (Ring, 1993).However, the algorithm could not learn arbitrary temporal contingencies.This paper describes Recurrent Transition Hierarchies (RTH), a learning method that combines several properties desirable for agents that must learn as they go.In particular, it learns online and incrementally, autonomously discovering new features as learning progresses.It requires no reset or episodes.It has a simple learning rule with update complexity linear in the number of parameters.

This paper describes a program of research that aims to develop and test healthcare robots for elder care. We describe the aims of the project, the robots developed, and studies we have performed in HRI in elder care. We highlight research design issues that have become apparent in the retirement home setting when testing robots. These issues are relevant to robotics researchers wishing to evaluate the effects of robotic care on older people’s quality of life.

In this paper we present a full architecture to deploy efficiently a grid in a private cloud approach. We first give details about the resources constraints acquisition. We use Rich Internet Application (RIA) to access and/or modify the resources in a very user-friendly interface. Then, using the previous information, we explain how we can compute a dynamic deployment plan, that can be used either to build an optimal grid of computers or to give information to its scheduler. This plan is computed using pddl solver with various logical constraints obtained from the IT users through the RIA.