Archive for the ‘Search Trees’ Category

Monte-Carlo Tree Search for Multi-Player Games [Semantics as Multi-Player Game]

Saturday, December 20th, 2014

Monte-Carlo Tree Search for Multi-Player Games by Joseph Antonius Maria Nijssen.

From the introduction:

The topic of this thesis lies in the area of adversarial search in multi-player zero-sum domains, i.e., search in domains having players with conflicting goals. In order to focus on the issues of searching in this type of domains, we shift our attention to abstract games. These games provide a good test domain for Artificial Intelligence (AI). They offer a pure abstract competition (i.e., comparison), with an exact closed domain (i.e., well-defined rules). The games under investigation have the following two properties. (1) They are too complex to be solved with current means, and (2) the games have characteristics that can be formalized in computer programs. AI research has been quite successful in the field of two-player zero-sum games, such as chess, checkers, and Go. This has been achieved by developing two-player search techniques. However, many games do not belong to the area where these search techniques are unconditionally applicable. Multi-player games are an example of such domains. This thesis focuses on two different categories of multi-player games: (1) deterministic multi-player games with perfect information and (2) multi-player hide-and-seek games. In particular, it investigates how Monte-Carlo Tree Search can be improved for games in these two categories. This technique has achieved impressive results in computer Go, but has also shown to be beneficial in a range of other domains.

This chapter is structured as follows. First, an introduction to games and the role they play in the field of AI is provided in Section 1.1. An overview of different game properties is given in Section 1.2. Next, Section 1.3 defines the notion of multi-player games and discusses the two different categories of multi-player games that are investigated in this thesis. A brief introduction to search techniques for two-player and multi-player games is provided in Section 1.4. Subsequently, Section 1.5 defines the problem statement and four research questions. Finally, an overview of this thesis is provided in Section 1.6.

This thesis is great background reading on the use of Monte-Carol tree search in games. While reading the first chapter, I realized that assigning semantics to a token is an instance of a multi-player game with hidden information. That is the “semantic” of any token doesn’t exist in some Platonic universe but rather is the result of some N number of players who also accept a particular semantic for some given token in a particular context. And we lack knowledge of the semantic and the reasons for it that will be assigned by some N number of players, which may change over time and context.

The semiotic triangle of Ogden and Richards (The Meaning of Meaning):

300px-Ogden_semiotic_triangle

for any given symbol, represents the view of a single speaker. But as Ogden and Richards note, what is heard by listeners should be represented by multiple semiotic triangles:

Normally, whenever we hear anything said we spring spontaneously to an immediate conclusion, namely, that the speaker is referring to what we should be referring to were we speaking the words ourselves. In some cases this interpretation may be correct; this will prove to be what he has referred to. But in most discussions which attempt greater subtleties than could be handled in a gesture language this will not be so. (The Meaning of Meaning, page 15 of the 1923 edition)

Is RDF/OWL more subtle than can be handled by a gesture language? If you think so then you have discovered one of the central problems with the Semantic Web and any other universal semantic proposal.

Not that topic maps escape a similar accusation, but with topic maps you can encode additional semiotic triangles in an effort to avoid confusion, at least to the extent of funding and interest. And if you aren’t trying to avoid confusion, you can supply semiotic triangles that reach across understandings to convey additional information.

You can’t avoid confusion altogether nor can you achieve perfect communication with all listeners. But, for some defined set of confusions or listeners, you can do more than simply repeat your original statements in a louder voice.

Whether Monte-Carlo Tree searches will help deal with the multi-player nature of semantics isn’t clear but it is an alternative to repeating “…if everyone would use the same (my) system, the world would be better off…” ad nauseam.

I first saw this in a tweet by Ebenezer Fogus.

Binary Search Trees (Clojure)

Thursday, April 10th, 2014

Data Structures in Clojure: Binary Search Trees by Max Countryman.

From the post:

Trees Everywhere

So far we have talked about two fundamental and pervasive data structures: linked lists and hash tables. Here again we discuss another important data structure and one that you will find is quite common: trees. Trees offer a powerful way of organizing data and approaching certain problems. In particular, searching and traversal. Whether you know it or not, you no doubt use trees in your programs today. For instance, Clojure’s vectors are backed by a special kind of tree!

Here we will construct our own tree, just like with our linked list and hash table implementations. Specifically, our tree will be a kind of tree known as a Binary Search Tree (BST). Often when someone says tree, they mean a BST.

We will look the basic structure of our tree, how we insert things into it, and how we find them again. Then we will explore traversing, and finally, removing nodes. At the end of this tutorial you will have a basic, functioning Binary Search Tree, which will be the basis for further explorations later on in this series.

Another installment by Max on data structures in Clojure.

Enjoy!

Binary Search Tree

Friday, June 29th, 2012

Binary Search Tree by Stoimen Popov.

Nothing new but clearly explained and well illustrated, two qualities that make this post merit mentioning.

To say nothing of the related posts at the bottom of this one that cover related material in an equally effective manner.

BTW, if you do use these illustrations in slides or teaching, give credit where credit is due. It will encourage others to contribute as well.

balanced binary search trees exercise for algorithms and data structures class

Wednesday, November 30th, 2011

balanced binary search trees exercise for algorithms and data structures class by René Pichardt.

From the post:

I created some exercises regarding binary search trees. This time there is no coding involved. My experience from teaching former classes is that many people have a hard time understanding why trees are usefull and what the dangers of these trees is. Therefor I have created some straight forward exercises that nevertheless involve some work and will hopefully help the students to better understand and internalize the concepts of binary search tress which are in my oppinion one of the most fundamental and important concepts in a class about algorithms and data structures.

I visited René’s blog because of the Google n gram post but could not leave without mentioning these exercises.

Great teaching technique!

What parts of topic maps should be illustrated with similar exercises?

PS: Still working on it but I am thinking that the real power of topic maps lies in its lack of precision or rather that a topic map can be as precise or as loose as need be. No pre-set need to have a decidable outcome. Or perhaps rather, it can have a decidable outcome that is the decidable outcome because I say it is so. ­čśë

Cache-Oblivious Search Trees Project (Fractal Trees, TokuDB)

Thursday, November 3rd, 2011

Cache-Oblivious Search Trees Project (Fractal Trees, TokuDB)

I watched a very disappointing presentation on Fractal Trees (used by Tokutek in the TokuDB) and so went looking for better resources.

The project is described as:

We implemented a cache-oblivious dynamic search tree as an alternative to the ubiquitious B-tree. We used a binary tree with a “van Emde Boas” layout whose leaves point to intervals in a “packed memory structure”. The search tree supports efficient lookup, as well as efficient amortized insertion and deletion. Efficient implementation of a B-tree requires understanding the cache-line size and page size and is optimized for a specific memory hierarchy. In contrast, a cache-oblivious dynamic search tree contains no machine-dependent variables, performs well on any memory hierarchy, and requires minimal user-level memory management. For random insertion of data, the data structure performs better than the Berkeley DB and a good implementation of B-trees. Another advantage of my data structure is that the packed memory array maintains data in sorted order, allows sequential reads at high speeds, and data insertions and deletions with few data writes on average. In addition, the data structure is easy to implement because he employed memory mapping rather than making the data stored on disk be a single level store.

We also have designed cache-oblivious search trees for which the keys can be very long (imagine a key, such as a DNA sequence, that is larger than main memory), and trees into which data can be quickly inserted.

One essential difference is that the B-Tree supports random I/O and the Fractal Tree converts random I/O into sequential I/O, which operates at near disk bandwidth speeds.

At Tokutek, I would review the paper by Bradley C. Kuszmaul, How TokuDB Fractal Tree™ IndexesWork.

Impressive software for the right situation.

The background literature is interesting. Not sure if directly applicable to topic maps or not.