Behavioural aspects of term-rewriting systems

Imperial Users onl

Real-Time Evolutionary Learning of Cooperative Predator-Prey Strategies

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 a prey and predators domain, evolving adaptive team strategies for the predators in real time against a single prey opponent. Our learning system works by continually training and updating the predator strategies, one at a time for a designated length of time while the game us being played. We test the performance of the system for real-time learning of strategies in the prey and predators domain against a hand-coded prey opponent. We show that the resulting real-time team strategies are able to capture hand-coded prey of varying degrees of difficulty without any prior learning. The system is highly adaptive to change, capable of handling many different situations, and quickly learning to function in situations that it has never seen before

Heuristics for Optimising the Calculation of Hypervolume for Multi-objective Optimisation Problems

The fastest known algorithm for calculating the hypervolume of a set of solutions to a multi-objective optimization problem is the HSO algorithm (hypervolume by slicing objectives). However, the performance of HSO for a given front varies a lot depending on the order in which it processes the objectives in that front. We present and evaluate two alternative heuristics that each attempt to identify a good order for processing the objectives of a given front. We show that both heuristics make a substantial difference to the performance of HSO for randomly-generated and benchmark data in 5-9 objectives, and that they both enable HSO to reliably avoid the worst-case performance for those fronts. The enhanced HSO enable the use of hypervolume with larger populations in more objectives

Fixture-scheduling for the Australian Football League using a Multi-objective Evolutionary Algorithm

AFL football is a team sport that entertains millions and contributes a huge amount of money to the Australian economy. Scheduling games in the AFL is difficult, as a number of different, often conflicting, factors must be considered. In this paper, we propose the use of a multi-objective evolutionary algorithm for determining such a schedule. We detail the technical details needed to apply a multi-objective evolutionary algorithm to this problem and report on experiments that show the effectiveness of this approach. Comparison with actual schedules used in the AFL demonstrates that this approach could make a useful contribution

A Faster Algorithm for Calculating Hypervolume

We present an algorithm for calculating hypervolume exactly, the Hypervolume by Slicing Objectives (HSO) algorithm, that is faster than any that has previously been published. HSO processes objectives instead of points, an idea that has been considered before but that has never been properly evaluated in the literature. We show that both previously studied exact hypervolume algorithms are exponential in at least the number of objectives and that although HSO is also exponential in the number of objectives in the worst case, it runs in significantly less time, i.e., two to three orders of magnitude less for randomly generated and benchmark data in three to eight objectives. Thus, HSO increases the utility of hypervolume, both as a metric for general optimization algorithms and as a diversity mechanism for evolutionary algorithm

Multi-objective strip packing using an evolutionary algorithm

Good algorithms exist for solving the strip packing problem when the objective is to minimise the amount of wasted material. We describe a multi-objective evolutionary algorithm for strip packing (MOSP) that optimises not only for wastage, but also for the operating speed of the cutting equipment, by minimising the number of independent cuts required by a packing. We show that MOSP returns a set of packings offering a range of trade-offs between the two objectives, and also that, by using heuristics that consider cuts, it derives packings with wastage levels that are better than most previously-published algorithms that optimise for wastage alone

Designing Comminution Circuits with a Multi-Objective Evolutionary Algorithm

Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design decisions are typically guided more by intuition and experience than by analysis. In this paper, we motivate the use of an evolutionary algorithm to aid in the design of such plants. We formalise plant design in terms suitable for application in a multi-objective evolutionary algorithm and create a simulation to assess the performance of candidate solutions. Results show the effectiveness of this approach with our algorithm producing designs superior to those used in practice today, promising significant financial benefits