Optimal Color Range Reporting in One Dimension

Color (or categorical) range reporting is a variant of the orthogonal range reporting problem in which every point in the input is assigned a \emph{color}. While the answer to an orthogonal point reporting query contains all points in the query range $Q$, the answer to a color reporting query contains only distinct colors of points in $Q$. In this paper we describe an O(N)-space data structure that answers one-dimensional color reporting queries in optimal $O(k+1)$ time, where $k$ is the number of colors in the answer and $N$ is the number of points in the data structure. Our result can be also dynamized and extended to the external memory model

Building a no limit Texas hold'em poker agent based on game logs using supervised learning

The development of competitive artificial Poker players is a challenge to Artificial Intelligence (AI) because the agent must deal with unreliable information and deception which make it essential to model the opponents to achieve good results. In this paper we propose the creation of an artificial Poker player through the analysis of past games between human players, with money involved. To accomplish this goal, we defined a classification problem that associates a given game state with the action that was performed by the player. To validate and test the defined player model, an agent that follows the learned tactic was created. The agent approximately follows the tactics from the human players, thus validating this model. However, this approach alone is insufficient to create a competitive agent, as generated strategies are static, meaning that they can't adapt to different situations. To solve this problem, we created an agent that uses a strategy that combines several tactics from different players. By using the combined strategy, the agentgreatly improved its performance against adversaries capable of modeling opponents

The Complexity of Approximating a Trembling Hand Perfect Equilibrium of a Multi-player Game in Strategic Form

We consider the task of computing an approximation of a trembling hand perfect equilibrium for an n-player game in strategic form, n >= 3. We show that this task is complete for the complexity class FIXP_a. In particular, the task is polynomial time equivalent to the task of computing an approximation of a Nash equilibrium in strategic form games with three (or more) players.Comment: conference version to appear at SAGT'1

Some Results on Average-Case Hardness Within the Polynomial Hierarchy

Abstract. We prove several results about the average-case complexity of problems in the Polynomial Hierarchy (PH). We give a connection among average-case, worst-case, and non-uniform complexity of optimization problems. Specifically, we show that if P NP is hard in the worst-case then it is either hard on the average (in the sense of Levin) or it is non-uniformly hard (i.e. it does not have small circuits). Recently, Gutfreund, Shaltiel and Ta-Shma (IEEE Conference on Computational Complexity, 2005) showed an interesting worst-case to averagecase connection for languages in NP, under a notion of average-case hardness defined using uniform adversaries. We show that extending their connection to hardness against quasi-polynomial time would imply that NEXP doesn’t have polynomial-size circuits. Finally we prove an unconditional average-case hardness result. We show that for each k, there is an explicit language in P Σ2 which is hard on average for circuits of size n k.

Ground state properties of a Tonks-Girardeau Gas in a periodic potential

In this paper, we investigate the ground-state properties of a bosonic Tonks-Girardeau gas confined in a one-dimensional periodic potential. The single-particle reduced density matrix is computed numerically for systems up to $N=265$ bosons. Scaling analysis of the occupation number of the lowest orbital shows that there are no Bose-Einstein Condensation(BEC) for the periodically trapped TG gas in both commensurate and incommensurate cases. We find that, in the commensurate case, the scaling exponents of the occupation number of the lowest orbital, the amplitude of the lowest orbital and the zero-momentum peak height with the particle numbers are 0, -0.5 and 1, respectively, while in the incommensurate case, they are 0.5, -0.5 and 1.5, respectively. These exponents are related to each other in a universal relation.Comment: 9 pages, 10 figure

Hilbert’s thirteenth problem and circuit complexity

We study the following question, communicated to us by Miklós Ajtai: Can all explicit (e.g., polynomial time computable) functions f: ({0,1} w )3 →{0,1} w be computed by word circuits of constant size? A word circuit is an acyclic circuit where each wire holds a word (i.e., an element of {0,1} w ) and each gate G computes some binary operation gG:({0,1}w)2→{0,1}w , defined for all word lengths w. We present an explicit function so that its w’th slice for any w ≥ 8 cannot be computed by word circuits with at most 4 gates. Also, we formally relate Ajtai’s question to open problems concerning ACC0 circuits

New Bounds for the Language Compression Problem

The CD complexity of a string x is the length of the shortest polynomial time program which accepts only the string x. The language compression problem consists of giving an upper bound on the CD A n complexity of all strings x in some set A. The best known upper bound for this problem is 2 log(jjA n jj) + O(log(n)), due to Buhrman and Fortnow. We show that the constant factor 2 in this bound is optimal. We also give new bounds for a certain kind of random sets R ` f0; 1g n , for which we show an upper bound of log(jjR n jj) + O(log(n)). 1 Introduction Kolmogorov complexity is a notion that measures the amount of regularity in a finite string. It has turned out to be a very useful tool in theoretical computer science. A simple counting argument showing that for each length there exist random strings, i.e. strings with no regularity, has had many applications (see [LV97]). Early in the history of computational complexity resource bounded notions of Kolmogorov complexity were..

On Computation and Communication with Small Bias

We present two results for computational models that allow error probabilities close to 1/2. First, most computational complexity classes have an analogous class in communication complexity. The class PP in fact has \emph{two}, a version with weakly restricted bias called PP\cc, and a version with unrestricted bias called UPP\cc. Ever since their introduction by Babai, Frankl, and Simon in 1986, it has been open whether these classes are the same. We show that PP\cc $\subsetneq$ UPP\cc. Our proof combines a query complexity separation due to Beigel with a technique of Razborov that translates the acceptance probability of \emph{quantum} protocols to polynomials. Second, we study how small the bias of minimal-degree polynomials that sign-represent Boolean functions needs to be. We show that the worst-case bias is at worst double-exponentially small in the sign-degree (which was very recently shown to be optimal by Podolski), while the average-case bias can be made single-exponentially small in the sign-degree (which we show to be close to optimal)