Enhancing history-based move ordering in game playing using adaptive data structures

This paper pioneers the avenue of enhancing a well-known paradigm in game playing, namely the use of History-based heuristics, with a totally-unrelated area of computer science, the field of Adaptive Data Structures (ADSs). It is a well-known fact that highly-regarded game playing strategies, such as alpha-beta search, benefit strongly from proper move ordering, and from this perspective, the History heuristic is, probably, one of the most acclaimed techniques used to achieve AI-based game playing. Recently, the authors of this present paper have shown that techniques derived from the field of ADSs, which are concerned with query optimization in a data structure, can be applied to move ordering in multi-player games. This was accomplished by ranking opponent threat levels. The work presented in this paper seeks to extend the utility of ADS-based techniques to two-player and multi-player games, through the development of a new move ordering strategy that incorporates the historical advantages of the moves. The resultant technique, the History-ADS heuristic, has been found to produce substantial (i.e, even up to 70%) savings in a variety of two-player and multi-player games, at varying ply depths, and at both initial and midgame board states. As far as we know, results of this nature have not been reported in the literature before

Novel AI strategies for Multi-Player games at intermediate board states

This paper considers the problem of designing efficient AI strategies for playing games at intermediate board states. While general heuristic-based methods are applicable for all boards states, the search required in an alpha-beta scheme depends heavily on the move ordering. Determining the best move ordering to be used in the search is particularly interesting and complex in an intermediate board state, compared to the situation where the game starts with an initial board state, as we do not assume the availability of “Opening book” moves. Furthermore, unlike the two-player scenario that is traditionally analyzed, we investigate the more complex scenario when the game is a multi-player game, like Chinese Checkers. One recent approach, the Best-Reply Search (BRS), resolves this by a process of grouping opponents, which although successful, incurs a very large branching factor. To address this, the authors of this work earlier proposed the Threat-ADS move ordering heuristic, by augmenting the BRS by invoking techniques from the field of Adaptive Data Structures (ADSs) to order the moves. Indeed, the Threat-ADS performs well under a variety of parameters when the game was analyzed at or near the game’s initial state. This work demonstrates that the Threat-ADS also serves as a solution to the unresolved question of finding a viable solution in the far-more variable, intermediate game states. Our present results confirm that the Threat-ADS performs well in these intermediate states for various games. Surprisingly, it, in fact, performs better in some cases, when compared to the start of the game

On Invoking Transitivity to Enhance the Pursuit-oriented Object Migration Automata

From the earliest studies in graph theory [2], [5], the phenomenon of transitivity has been used to design and analyze problems that can be mapped onto graphs. Some of the practical examples of this phenomenon are the “Transitive Closure” algorithm, the multiplication of Boolean matrices, the determination of Communicating States in Markov Chains etc. The use of transitivity, however, to catalyze the partitioning problems is, to our knowledge, unreported, and it is by no means trivial considering the pairwise occurrences of the queries in the query stream. This paper pioneers such a mechanism. In particular, we consider the Object Migrating Automaton (OMA) that has been used for decades to solve the Equi-Partitioning Problem (EPP) where W objects are placed in R partitions of equal sizes so that objects accessed together fall in to the same partition. The OMA, which encountered certain deadlock configurations, was enhanced by Gale et al. to yield the Enhanced OMA (EOMA). Both the OMA and the EOMA were significantly improved by incorporating into them, the recently-introduced “Pursuit” phenomenon from the field of Learning Automata (LA). In this paper1 we shall show that the Pursuit matrix that consists of the estimates of the probabilities of the pairs presented to the LA, possesses the property of transitivity akin to the property found in graph-related problems. By making use of this observation, transitive-closure-like arguments can be made to invoke reward and penalty operations on the POMA and the PEOMA. This implies that objects can be moved towards their correct partitions even when the system is dormant, i.e., when the Environment does not present any queries or partitioning information to the learning algorithm. The results that we present demonstrate that the newly-designed transitive-based algorithms are about 20% faster than their non-transitive versions. As far as we know, these are the fastest partitioning algorithms to-date

On using adaptive Binary Search Trees to enhance self organizing maps

We present a strategy by which a Self-OrganizingMap (SOM) with an underlying Binary Search Tree (BST) structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length (WPL) of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution, and additionally, the neighborhood properties of the neurons suit the best BST that represents the data

Semi-supervised classification using tree-based self-organizing maps

This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabeled and labeled instances. First, we learn the structure of the data distribution in an unsupervised manner. After convergence, and once labeled data become available, our strategy tags each of the clusters according to the evidence provided by the instances. Unlike other neighborhood-based schemes, our classifier uses only a small set of representatives whose cardinality can be much smaller than that of the input set. Our experiments show that, on average, the accuracy of such classifier is reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes

Identifying unreliable sensors without a knowledge of the ground truth in deceptive environments

This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping it onto the fascinating paradox of trying to identify stochastic liars without any additional information about the truth. Even though that work was significant, it was constrained by the model in which we are living in a world where “the truth prevails over lying”. Couched in the terminology of Learning Automata (LA), this corresponds to the Environment (Since the Environment is treated as an entity in its own right, we choose to capitalize it, rather than refer to it as an “environment”, i.e., as an abstract concept.) being “Stochastically Informative”. However, as explained in the paper, solving the problem under the condition that the Environment is “Stochastically Decepti”, as opposed to informative, is far from trivial. In this paper, we provide a solution to the problem where the Environment is deceptive (We are not aware of any other solution to this problem (within this setting), and so we believe that our solution is both pioneering and novel.), i.e., when we are living in a world where “lying prevails over the truth”

On the online classification of data streams using weak estimators

In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances, thus requiring us to update the training set and the training model. The results obtained from rigorous empirical analysis on multinomial distributions, is remarkable. Indeed, it demonstrates the applicability of our method on synthetic datasets, and proves the advantages of the introduced scheme

A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids

In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. In our specific instantiation, we consider the scenario when the power system has a local-area Smart Grid (SG) subnet comprising of a single power source and multiple customers. The objective of the exercise is to tacitly control the total power consumption of the customers’ shiftable loads so to approach the rigid power budget determined by the power source, but to simultaneously not exceed this threshold. As opposed to the “traditional” paradigm that utilizes a central controller to achieve the load scheduling, we seek to achieve this by pursuing a distributed approach that allows the users¹ to make individual decisions by invoking negotiations with other customers. The decisions are essentially of the sort where the individual users can choose whether they want to be supplied or not. From a modeling perspective, the distributed scheduling problem is formulated as a game, and in particular, a so-called “Potential” game. This game has at least one pure strategy Nash Equilibrium (NE), and we demonstrate that the NE point is a global optimal point. The solution that we propose, which utilize

Concept drift detection using online histogram-based bayesian classifiers

In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of information theory to automatically identify changes in the performance of the classifier, and consequently, forces the reconstruction of the classification model in run-time as and when it is needed. These properties have been confirmed experimentally over numerous data sets (In the interest of space and brevity, we present here only a subset of the available results. More detailed results are found in [2].) from different domains. As far as we know, our histogram-based Naïve Bayes classification paradigm for time-varying datasets is both novel and of a pioneering sort

Learning automaton based on-line discovery and tracking of spatio-temporal event patterns

Discovering and tracking of spatio-temporal patterns in noisy sequences of events is a difficult task that has become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social networking applications. The core activities for applications of this class include the sharing and notification of events, and the importance and usefulness of these functionalites increases as event-sharing expands into larger areas of one's life. Ironical