Evolving Players for an Ancient Game: Hnefatafl

Hnefatafl is an ancient Norse game - an ancestor of chess. In this paper, we report on the development of computer players for this game. In the spirit of Blondie24, we evolve neural networks as board evaluation functions for different versions of the game. An unusual aspect of this game is that there is no general agreement on the rules: it is no longer much played, and game historians attempt to infer the rules from scraps of historical texts, with ambiguities often resolved on gut feeling as to what the rules must have been in order to achieve a balanced game. We offer the evolutionary method as a means by which to judge the merits of alternative rule set

Iterated Prisoner\u27s Dilemma for Species

The Iterated Prisoner\u27s Dilemma (IPD) is widely used to study the evolution of cooperation between self-interested agents. Existing work asks how genes that code for cooperation arise and spread through a single-species population of IPD playing agents. In this paper, we focus on competition between different species of agents. Making this distinction allows us to separate and examine macroevolutionary phenomena. We illustrate with some species-level simulation experiments with agents that use well-known strategies, and with species of agents that use team strategies

Evolving Group Strategies for IPD

The Iterated Prisoners Dilemma (IPD) is often used to model cooperation between self-interested agents. In an earlier study, we introduced a framework using IPD to study the effects of species-level competition on the evolution of cooperative behaviour. In this paper, we extend the previous work, using co-evolutionary simulations of interactions between species of IPD-playing agents to investigate how group strategies may evolve. We find that the ability to cooperate more with agents of the same species greatly increases the ferocity of competition between species

Inference of regular languages using model simplicity

We describe an approach that is related to a number of existing algorithms for the inference of a regular language from a set of positive (and optionally also negative) examples. Variations on this approach provide a family of algorithms that attempt to minimise the complexity of a description of the example data in terms of a finite state automaton model. Experiments using a standard set of small problems show that this approach produces satisfactory results when positive examples only are given, and can be helpful when only a limited number of negative examples is available. The results also suggest that improved algorithms will be needed in order to tackle more challenging problems, such as data mining and exploratory sequential analysis application

Enumerating Knight\u27s Tours using an Ant Colony Algorithm

In this paper, we show how an ant colony optimisation algorithm may be used to enumerate knight\u27s tours for variously sized chessboards. We have used the algorithm to enumerate all tours on 5×5 and 6×6 boards, and, while the number of tours on an 8×8 board is too large for a full enumeration, our experiments suggest that the algorithm is able to uniformly sample tours at a constant, fast rate for as long as is desired

Tego - A framework for adversarial planning

This study establishes a framework called ∗-Tego for a situation in which two agents are each given a set of players for a competitive game. Each agent places their players in an order. Players on each side at the same position in the order play one another, with the agent\u27s score being the sum of their player\u27s scores. The planning agents are permitted to simultaneous reorder their players in each of several stages. The reordering is termed competitive replanning. The resulting framework is scalable by changing the number of players and the complexity of the replanning process. The framework is demonstrated using iterated prisoner\u27s dilemma on a set of twenty players. The system is first tested with one agent unable to change the order of its players, yielding an optimization problem. The system is then tested in a competitive co-evolution of planning agents. The optimization form of the system makes globally sensible assignments of players. The co-evolutionary version concentrates on matching particular high-payoff pairs of players with the agents repeatedly reversing one another\u27s assignments, with the majority of players with smaller payoffs at risk are largely ignored

Using NEAT for Continuous Adaptation and Teamwork Formation in Pacman

Despite games often being used as a testbed for new computational intelligence techniques, the majority of artificial intelligence in commercial games is scripted. This means that the computer agents are non-adaptive and often inherently exploitable because of it. In this paper, we describe a learning system designed for team strategy development in a real time multi-agent domain. We test our system in the game of Pacman, evolving adaptive strategies for the ghosts in simulated real time against a competent Pacman player. Our agents (the ghosts) are controlled by neural networks, whose weights and structure are incrementally evolved via an implementation of the NEAT (Neuro-Evolution of Augmenting Topologies) algorithm. We demonstrate the design and successful implementation of this system by evolving a number of interesting and complex team strategies that outperform the ghosts\u27 strategies of the original arcade version of the game

RedTNet: A network model for strategy games

In this work, we develop a simple, graph-based framework, RedTNet, for computational modeling of strategy games and simulations. The framework applies the concept of red teaming as a means by which to explore alternative strategies. We show how the model supports computer-based red teaming in several applications: realtime strategy games and critical infrastructure protection, using an evolutionary algorithm to automatically detect good and often surprising strategies

Evolving Crushers

This paper describes the use of an evolutionary algorithm to solve an engineering design problem. The problem involves determining the geometry and operating settings for a crusher in a comminution circuit for ore processing. The intention is to provide a tool for consulting engineers that can be used to explore candidate designs for various scenarios. The algorithm has proved capable of deriving designs that are clearly superior to existing designs, promising significant financial benefit

Mobile games with intelligence: a killer application?

Mobile gaming is an arena full of innovation, with developers exploring new kinds of games, with new kinds of interaction between the mobile device, players, and the connected world that they live in and move through. The mobile gaming world is a perfect playground for AI and CI, generating a maelstrom of data for games that use adaptation, learning and smart content creation. In this paper, we explore this potential killer application for mobile intelligence. We propose combining small, light-weight AI/CI libraries with AI/CI services in the cloud for the heavy lifting. To make our ideas more concrete, we describe a new mobile game that we built that shows how this can work