We examine the utility of a curriculum (a means of presenting training samples in a meaningful order) in unsupervised learning of probabilistic grammars. We introduce the {\em incremental construction hypothesis} that explains the benefits of a curriculum in learning grammars and offers some useful insights into the design of curricula as well as learning algorithms. We present results of experiments with (a) carefully crafted synthetic data that provide support for our hypothesis and (b) natural language corpus that demonstrate the utility of curricula in unsupervised learning of probabilistic grammars.

Many systems use Markov models to generate finite-length sequences that imitate a given style. These systems often need to enforce specific control constraints on the sequences to generate. Unfortunately, control constraints are not compatible with Markov models, as they induce long-range dependencies that violate the Markov hypothesis of limited memory. Attempts to solve this issue using heuristic search do not give any guarantee on the nature and probability of the sequences generated. We propose a novel and efficient approach to controlled Markov generation for a specific class of control constraints that 1) guarantees that generated sequences satisfy control constraints and 2) follow the statistical distribution of the initial Markov model. Revisiting Markov generation in the framework of constraint satisfaction, we show how constraints can be compiled into a non-homogeneous Markov model, using arc-consistency techniques and renormalization. We illustrate the approach on a melody generation problem and sketch some realtime applications in which control constraints are given by gesture controllers.

It is well known that cheating occurs in sports. In cup competitions, a common type of sports competition, one method of cheating is in manipulating the seeding to unfairly advantage a particular team. Previous empirical and theoretical studies of seeding manipulation have focused on competitions with unrestricted seeding. However, real cup competitions often place restrictions on seedings to ensure fairness, wide geographic interest, and so on. In this paper, we perform an extensive empirical study of seeding manipulation under comprehensive and realistic sets of restrictions. A generalized random model of competition problems is proposed. This model creates a realistic range of problem instances that are used to identify the sets of seeding restrictions that are hard to manipulate in practice. We end with a discussion of the implications of this work and recommendations for organizing competitions so as to prevent or reduce the opportunities for manipulating the seeding.

Car pollution is one of the major causes of green-house emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Existing efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that min- imize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important fea- ture when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.

Many important problems can be compactly represented as quantified boolean formulas (QBF) and solved by general QBF solvers. To date QBF solvers have mainly focused on determining whether or not the input QBF is true or false. However, additional important information about an application can be gathered from its QBF formulation. In this paper we demonstrate that a circuit-based QBF solver can be exploited to obtain a Q-Resolution proof of the truth or the falsity of a QBF. QBFs have a natural interpretation as a two person game and our main result is to show how, via a simple computation, the moves for the winning player can be computed directly from these proofs. This result shows that the proof is a representation of the winning strategy. In previous approaches the winning strategy has often been represented in a way that makes it hard to verify. In our approach the correctness of the strategy follows directly from the correctness of the proof, which is relatively easy to verify.

We present a new and compelling approach to the efficient solution of important computational problems that arise in the context of abstract argumentation. Our approach makes known algorithms defined for restricted fragments generally applicable, at a computational cost that scales with the distance from the fragment. Thus, in a certain sense, we gradually augment tractable fragments. Surprisingly, it turns out that some tractable fragments admit such an augmentation and that others do not. More specifically, we show that the problems of credulous and skeptical acceptance are fixed-parameter tractable when parameterized by the distance from the fragment of acyclic argumentation frameworks. Other tractable fragments such as the fragments of symmetrical and bipartite frameworks seem to prohibit an augmentation: the acceptance problems are already intractable for frameworks at distance 1 from the fragments. For our study we use a broad setting and consider several different semantics. For the algorithmic results we utilize recent advances in fixed-parameter tractability.

L1-regularized least squares, with the ability of discovering sparse representations, is quite prevalent in the field of machine learning, statistics and signal processing. In this paper, we propose a novel algorithm called Dual Projected Newton Method (DPNM) to solve the L1-regularized least squares problem. In DPNM, we first derive a new dual problem as a box constrained quadratic programming. Then, a projected Newton method is utilized to solve the dual problem, achieving a quadratic convergence rate . Moreover, we propose to utilize some practical techniques, thus it greatly reduces the computational cost and makes DPNM more efficient. Experimental results on six real-world data sets indicate that DPNM is very efficient for solving the L1-regularized least squares problem, by comparing it with state of the art methods.

Balanced multi-way number partitioning (BMNP) seeks to split a collection of numbers into subsets with (roughly) the same cardinality and subset sum. The problem is NP-hard, and there are several exact and approximate algorithms for it. However, existing exact algorithms solve only the simpler, balanced two-way number partitioning variant, whereas the most effective approximate algorithm, BLDM, may produce widely varying subset sums. In this paper, we introduce the LRM algorithm that lowers the expected spread in subset sums to one third that of BLDM for uniformly distributed numbers and odd subset cardinalities. We also propose Meld, a novel strategy for skewed number distributions. A combination of LRM and Meld leads to a heuristic technique that consistently achieves a narrower spread of subset sums than BLDM.

We present Constituent Grammatical Evolution (CGE), a new evolutionary automatic programming algorithm that extends the standard Grammatical Evolution algorithm by incorporating the concepts of constituent genes and conditional behaviour-switching. CGE builds from elementary and more complex building blocks a control program which dictates the behaviour of an agent and it is applicable to the class of problems where the subject of search is the behaviour of an agent in a given environment. It takes advantage of the powerful Grammatical Evolution feature of using a BNF grammar definition as a plug-in component to describe the output language to be produced by the system. The main benchmark problem in which CGE is evaluated is the Santa Fe Trail problem using a BNF grammar definition which defines a search space semantically equivalent with that of the original definition of the problem by Koza. Furthermore, CGE is evaluated on two additional problems, the Los Altos Hills and the Hampton Court Maze. The experimental results demonstrate that Constituent Grammatical Evolution outperforms the standard Grammatical Evolution algorithm in these problems, in terms of both efficiency (percent of solutions found) and effectiveness (number of required steps of solutions found).

Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. Recently, a different approach, RIBS, was suggested which, instead of learning costs to the goal, learns costs from the start state. RIBS can solve some problems faster, but in other problems has poor performance. We present a new algorithm, f-cost Learning Real-Time A* (f-LRTA*), which combines both approaches, simultaneously learning distances from the start and heuristics to the goal. An empirical evaluation demonstrates that f-LRTA* outperforms both RIBS and LRTA*-style approaches in a range of scenarios.