BrilliANT: An Entry for the GECCO’2007 Ant Wars Contest

نویسندگان

  • Wojciech Jaśkowski
  • Krzysztof Krawiec
  • Bartosz Wieloch
چکیده

One of our first observations concerning Ant Wars was that ant’s field of view (FOV) is rather small (24 fields when excluding the actual ant’s position). If one considers food locations only and takes into account rotational invariance and symmetry, there are 2/4/2 = 2 = 2097152 unique FOV states (when ignoring the constraint placed on the amount of food and the existence of opponent). As FOV occupies approx. 20.7% of the ant’s world and the total number of food pieces amounts to 15, the expected number of food pieces within FOV is slightly more than 3 when the game begins. Also, the probability of having n food pieces within FOV drops quickly as n increases and, for instance, for n = 8 it amounts to less than 0.5%. This further reduces the number of realistically possible FOV states by several orders of magnitude. This suggests that it is difficult to build a sophisticated strategy based only on the current FOV state. Probably more may be gained by virtually extending the FOV, i.e., keeping track of board state as the ant moves. To enable this, we equip our ants with memory, which involves the following three components, each of them implemented as a two-dimensional array overlayed over the board:

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Winning Ant Wars: Evolving a Human-Competitive Game Strategy Using Fitnessless Selection

We tell the story of BrilliAnt, the winner of the Ant Wars contest organized within GECCO’2007, Genetic and Evolutionary Computation Conference. The task for the AntWars contestants was to evolve a controller for a virtual ant that collects food in a square toroidal grid environment in the presence of a competing ant. BrilliAnt, submitted to the contest by our team, has been evolved through com...

متن کامل

Algorithms for Test-Based Problems

Problems in which some elementary entities interact with each other are common in computational intelligence. This scenario, typical for coevolving artificial-life agents, learning strategies for games, and machine learning from examples, can be formalized as a test-based problem and conveniently embedded in the common conceptual framework of coevolution. In test-based problems candidate soluti...

متن کامل

Ant Colony Programming for Approximation Problems

A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.

متن کامل

Solving Approximation Problems by Ant Colony Programming

A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.

متن کامل

Ant Algorithm For Construction Of Evolutionary Tree

This paper proposes an Ant algorithm implemented with stack count for the purpose of designing suffix representations of tree structures. The algorithm is applied to the problem of reconstructing an evolutionary tree from nucleotide sequences. The algorithm shows compatible results in simulated experiment and alignment of protein sequences from 15 species against the existing algorithms.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007