Advanced Data Structures Tutorial Data Courses

#artificialintelligence

Data structures are amongst the most fundamental ingredients in the recipe for creating efficient algorithms and good software design. Knowledge of how to create and design good data structures is an essential skill required in becoming an exemplary programmer. This course will teach you how to master the fundamental ideas surrounding data structures. This course provides you with high quality animated videos explaining a multitude of data structures and how they are represented visually. You will learn how to code various data structures together with simple to follow step-by-step instructions.


Improving your Algorithms & Data Structure Skills – Coderbyte – Medium

#artificialintelligence

Some of the resources in this article originally appeared in one of my comments on a reddit post that became quite popular. Here's the original thread, and my new write-up is below. The first thing you'll need if you want to get better at algorithms and data structures is a solid base. This base can be learned one of several ways, either through a computer science program at university, some coding bootcamps focus a bit on the topics below, or you can learn on your own from books, videos, or online lectures. So you'll need a basic understanding of the following topics to get started: Learn about arrays, linked lists, binary trees, hash tables, graphs, stacks, queues, heaps, and other fundamental data structures.



Coalitional Games via Network Flows

AAAI Conferences

We introduce a new representation scheme for coalitional games, called coalition-flow networks (CF-NETs), where the formation of effective coalitions in a task-based setting is reduced to the problem of directing flow through a network. We show that our representation is intuitive, fully expressive, and captures certain patterns in a significantly more concise manner compared to the conventional approach. Furthermore, our representation has the flexibility to express various classes of games, such as characteristic function games, coalitional games with overlapping coalitions, and coalitional games with agent types. As such, to the best of our knowledge, CF-NETs is the first representation that allows for switching conveniently and efficiently between overlapping/non-overlapping coalitions, with/without agent types. We demonstrate the efficiency of our scheme on the coalition structure generation problem, where near-optimal solutions for large instances can be found in a matter of seconds.


Approximate Coalition Structure Generation

AAAI Conferences

Coalition formation is a fundamental problem in multi-agent systems. In characteristic function games (CFGs), each coalition C of agents is assigned a value indicating the joint utility those agents will receive if C is formed. CFGs are an important class of cooperative games; however, determining the optimal coalition structure, partitioning of the agents into a set of coalitions that maximizes the social welfare, currently requires O (3 n ) time for n agents. In light of the high computational complexity of the coalition structure generation problem, a natural approach is to relax the optimality requirement and attempt to find an approximate solution that is guaranteed to be close to optimal. Unfortunately, it has been shown that guaranteeing a solution within any factor of the optimal requires Ω(2 n ) time. Thus, the best that can be hoped for is to find an algorithm that returns solutions that are guaranteed to be as close to the optimal as possible, in as close to O (2 n ) time as possible. This paper contributes to the state-of-the-art by presenting an algorithm that achieves better quality guarantees with lower worst case running times than all currently existing algorithms. Our approach is also the first algorithm to guarantee a constant factor approximation ratio, 1/8, in the optimal time of O (2 n . The previous best ratio obtainable in O (2 n ) was 2/ n .