General video game AI: Competition, challenges, and opportunities

The General Video Game AI framework and competition pose the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game-dependent heuristics. This short paper summarizes the motivation, infrastructure, results and future plans of General Video Game AI, stressing the findings and first conclusions drawn after two editions of our competition, and outlining our future plans

Fingerprinting Tabletop Games

We present some initial work on characterizing games using a visual 'fingerprint' generated from several independent optimisation runs over the parameters used in Monte Carlo Tree Search (MCTS). This 'fingerprint' provides a useful tool to compare games, as well as highlighting the relative sensitivity of a specific game to algorithmic variants of MCTS. The exploratory work presented here shows that in some games there is a major change in the optimal MCTS parameters when we move from 2-players to 3 or 4-players

Balancing Wargames through Predicting Unit Point Costs

In tactical wargames, such as Warhammer 40K, two or more players control asymmetrical armies that include multiple units of different types and strengths. In these type of games, unit are assigned point costs, which are used to ensure that all players will control armies of similar strength. Players are provided with a total budget of points they can spend to purchase units that will be part of their army lists. Calculating the point value of individual units is a tedious manual process, which often requires long play-testing sessions and iterations of adjustments. In this paper, we propose an automated way of predicting these point costs using a linear regression approach. We use a multi-unit, turn-based, non-balanced game that has three asymmetric armies. We use Monte Carlo Tree Search agents to simulate the players, using different heuristics to emulate playing strategies. We present six different variants of our unit-point prediction algorithm, and we show how our best variant is able to almost reduce the unbalanced nature of the game by half

Skill Depth in Tabletop Board Games

There are well-established methods for rating the relative skill of players such as Elo or TrueSkill ratings. This is not the case for rating games by their relative difficulty, or the level of 'skill' required to play them well. Previous work has proposed skill-traces as an answer to this question, which use data from games played between agents with progressively higher computational budgets to estimate the difficulty of the game (or skill-depth). We try to improve on previous work by expanding the algorithmic space considered and that this can radically change the ratings of some games. We then propose a new parameterised model for the level of skill a game requires and test this on a suite of multiplayer tabletop board games, concluding that the parameters can be usefully interpreted and provide a slightly better fit to human-estimates

A case study in AI-assisted board game design

We use AI agents to play successive design iterations of an analogue board game to understand the sorts of question a designer asks of a game, and how AI play-testing approaches can help answer these questions and reduce the need for time-consuming human play-testing. Our case study supports the view that AI play-testing can complement human testing, but can certainly not replace it. A core issue to be addressed is the extent to which the designer trusts the results of AI play-testing as sufficiently human-like. The majority of design changes are inspired from human play-testing, but AI play-testing helpfully complements these and often gave the designer the confidence to make changes faster where AI and humans 'agreed'

DNA fingerprinting of S. typhimurium from a pig longitudinal study

A 400 sow farrow-to-finish farm was sampled for 6 consecutive years to determine the persistence of Salmonella Typhimurium (STM) DT104. Pulsed-field gel electrophoresis, plasmid DNA analysis and antibiotic resistance phenotyping was carried out on selected STM strains, isolated from the farm during the time of the study. Clonal persistence as well and introduction of new clones from external sources were proven to be the main mechanisms by which salmonella infection was maintained in the farm

Porcine associated Salmonella Typhimuirum DT120: use of PFGE and MLVA in a putative outbreak investigation

In November 2006 a cluster of Salmonella Typhimurium DT120 in the North East of England was putatively associated with the consumption of pork. At the same time cases of illness in Denmark were associated with this Salmonella type, and a EU alert was issued to determine the type of S. Typhimurium DT120 identified. Isolates from the UK and Denmark were compared on the basis of antibiogram, Pulsed Field Gel Electrophoresis (PFGE) and Multi-Locus-Variable number andem repeat Analysis (MLVA or VNTR) to identify the S. Typhimurium DT120 type and results were compared electronically. Isolates from England had the resistance profile ApSSuT (ampicillin, streptomycin, sulfamethoxazole and tetracycline), VNTR profile (171-244-316-0-487) and with the distinct PFGE type (STYMXB.0083). Isolates from Denmark were resistant to Ap (ampicillin) only, had the VNTR type (171-270-324-0-490) and a PFGE type distinct from England (STYMXB.0010). It was therefore possible to confirm that the isolates from England and Denmark were not identical. These results have verified the significance of VNTR in outbreak investigations for S. Typhimurium and have demonstrated how new molecular strategies may be used to supplement existing methods such as PFGE to enable the accurate and rapid comparison of isolates from different countries