Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over 600 generated instances of different types. Our results reveal interesting links between the network measures, the autocorrelation measures and the performance of heuristic search algorithms.

Local Optima Networks (LONs) have been recently proposed as an alternative model of combinatorial fitness landscapes. The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. A new set of metrics can be derived from this model that capture the distribution and connectivity of the local optima in the underlying configuration space. This paper departs from the descriptive analysis of local optima networks, and actively studies the correlation between network features and the performance of a local search heuristic. The NK family of landscapes and the Iterated Local Search metaheuristic are considered. With a statistically-sound approach based on multiple linear regression, it is shown that some LONs' features strongly influence and can even partly predict the performance of a heuristic search algorithm. This study validates the expressive power of LONs as a model of combinatorial fitness landscapes.

NICTA is Australia's Information and Communications Technology (ICT) Centre of Excellence. It is the largest organization in Australia dedicated to ICT research. While it has close links with local universities, it is in fact an independent but not-for-profit company in the business of doing research, commercializing that research and training PhD students to do that research. Much of the work taking place at NICTA involves various topics in artificial intelligence. In this article, we survey some of the AI work being undertaken at NICTA.

Real-time strategy (RTS) games are known to be one of the most complex game genres for humans to play, as well as one of the most difficult games for computer AI agents to play well. To tackle the task of applying AI to RTS games, recent techniques have focused on a divide-and-conquer approach, splitting the game into strategic components, and developing separate systems to solve each. This trend gives rise to a new problem: how to tie these systems together into a functional real-time strategy game playing agent. In this paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.

Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method — Alpha-Beta search for durative moves— that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable — opening the door for a promising line of research on opponent combat modelling.

Search-based procedural content generation methods allow video games to introduce new content continually, thereby engaging the player for a longer time while reducing the burden on developers. However, games so far have not explored the potential economic value of unique evolved artifacts. Building on this insight, this paper presents for the first time a Facebook game called Petalz in which players can share flowers they breed themselves with other players through a global marketplace. In particular, the market in this social game allows players to set the price of their evolved aesthetically-pleasing flowers in virtual currency. Furthermore, the transaction in which one player buys seeds from another creates a new social element that links the players in the transaction. The combination of unique user-generated content and social gaming in Petalz facilitates meaningful collaboration between users, positively influences the dynamics of the game, and opens new possibilities in digital entertainment.

KGoldrunner is a puzzle-oriented platform game with dynamic elements. This paper describes Goldspinner, an automatic level generation system for KGoldrunner. Goldspinner has two parts: a genetic algorithm that generates candidate levels, and simulations that use an AI agent to attempt to solve the level from the player's perspective. Our genetic algorithm determines how "good" a candidate level is by examining many different properties of the level, all based on its static aspects. Once the genetic algorithm identifies a good candidate, simulations are performed to evaluate the dynamic aspects of the level. Levels that are statically good may not be dynamically good (or even solvable), making simulation an essential aspect of our level generation system. By carefully optimizing our genetic algorithm and simulation agent we have created an efficient system capable of generating interesting levels in real time.

TRANSIT is a fast and optimal technique for computing shortest path costs in road networks. It is attractive for its usually modest memory requirements and impressive running times. In this paper we give a first analysis of TRANSIT routing on a set of popular grid-based video-game benchmarks taken from the AI pathfinding literature. We show that in the presence of path symmetries, which are inherent to most grids but normally not road networks, TRANSIT is strongly and negatively impacted, both in terms of performance and memory requirements. We address this problem by developing a new general symmetry breaking technique which adds small random epsilon-values to edges in the search graph, reducing the size of the TRANSIT network by up to 4 times while preserving optimality. Using our enhancements TRANSIT achieves up to four orders of magnitude speed improvement vs. A* search and uses in many cases only a small (<=10MB) or modest (<= 50MB) amount of memory. We also compare TRANSIT with CPDs, a recent and very fast database-driven pathfinding approach. We find the algorithms have complementary strengths but also identify a class of problems for which TRANSIT is up to two orders of magnitude faster than CPDs using a comparable amount of memory.

We describe a heuristic search technique for multi-agent pursuit-evasion games in partially observable Euclidean space where a team of trackers attempt to minimize their uncertainty about an evasive target. Agents' movement and observation capabilities are restricted by polygonal obstacles, while each agent's knowledge of the other agents is limited to direct observation or periodic updates from team members. Our polynomial-time algorithm is able to generate strategies for games in continuous two-dimensional Euclidean space, an improvement over past algorithms that were only applicable to simple gridworld domains. We demonstrate that our algorithm is tolerant of interruptions in communication between agents, continuing to generate good strategies despite long periods of time where agents are unable to communicate directly. Experiments also show that our technique generates effective strategies quickly, with decision times of less than a second for reasonably sized domains with six or more agents.

The Intentional Fast-Forward (IFF) planner is an attempt to apply fast forward-chaining state-space search methods to intentional planning---planning such that every action is directed toward some character's goal. The IFF heuristic is based on Hoffmann's original Fast Forward heuristic (2001), which solves a simplified version of the problem and uses that solution as a guide for the real problem. IFF incorporates constraints imposed by intentional planning to narrow down the set of steps which can be taken next, and it identifies fruitless branches of the search space early.