Deck Creation

As a final experiment, we attempted to create a selection of novel decklists by providing a

small set of cards and allowing the algorithms above to generate the decks. In each case, a

single Identity card was provided, along with three other cards in the correct faction which

are commonly played in tournament play. The generated decks were then shown to a group

of experienced players, who were asked to criticise the decklists. All of the generated decks

are listed in Appendix B.

In each case, the experienced players were content that the decks were reasonable for

play in a casual or semi-competitive environment, but may struggle in a highly competitive

tournament environment. They agreed that there were no completely inappropriate inclu-

sions, however there were a few questionable choices, and in some cases it appeared that

some card combinations had been left unfinished in the decks. When asked to provide mod-

ifications to the decks to improve their quality, the players each suggest 1-3 card exchanges,

stating that those changes would likely make the decks worthy of play in a more competitive

6.3

Summary & Discussion

Our attempts to adequately predict the number of duplicate cards within a deck have been

some what effective, but there is still work to be done here, as our best prediction is based on

our heuristic knowledge of the specific card, rather than knowledge of the card in context.

Successfully adding contextual heuristic knowledge into this process will surely lead to

more accurate prediction.

Given that experienced players found our decks to be at least worthy of consideration

for play in a tournament, there is definite potential in taking this technology forward, and

potentially creating tools for the Netrunner community to generate and validate their decks.

With a little adaptation, this can also be taken into other similar games.

As discussed earlier in this chapter, knowledge of the content of an opponent’s deck

represents a potentially powerful strategic knowledge which can be exploited to significant

advantage. As such, we would expect an agent which can successfully predict the content

of an opponent’s deck to be significantly stronger than an agent which was unable to do so.

It is also worth noting however, that a misprediction could cause a significant reduction in

agent strength.

6.3.1

Contributions

Our principle research contribution from our work on Rule Association Mining is a substan-

tial improvement in deck prediction from the default apriori algorithm. It can be seen that

our modifications to the Apriori technique provide a significant improvement to prediction

of decks in Netrunner, showing a maximum improvement of ∼13% between the default

apriori algorithm (a1) and our optimal modified algorithm (a7).

ously, the ability to predict opponent decks can be a powerful strategic advantage, but this

can also be leveraged in other ways. An oppositional agent that attempts to predict the hu-

man player’s deck rather than “cheating” and looking is likely to feel more fair (and more

interesting) to the human player, assuming this is correctly measured. This contribution

goes further than merely deck prediction, as these techniques can also be used as a form of

deckbuilding aide, to help new players build decks and guide them as to what might be sen-

sible inclusions in their first decks. Given that deck building is a challenging task for human

players, any help provided by an machine learning agent would likely provide significant

learning for new players and assistance for experienced players in the process of building a

deck.

Similar principles could also be used in other computer games such as MOBAs (Multi-

player Online Battle Arena) and RTS (Real-time Strategy) games. Both these archetypes of

games use highly customisable and configurable Build Orders which dictate the sequence in

which actions should be taken for optimal performance. Determining build order could be

Conclusions & Further Work

In this thesis, we have investigated the application of specific artificial intelligence tech-

niques to creating differing playstyle in games. Our principle body of work has been the

modification of MCTS in order to affect play style, and thus create entertaining or interest-

ing differences in play between a modified agent and a unmodified agent. In order to do so,

we have considered a variety of techniques including heuristic pruning and the modification

of the action selection mechanism. This work is motivated in large part by the needs of

the commercial games industry and particularly those of my sponsoring company, Stainless

Games. Commercial games need entertaining AI opponents, not merely strong ones, and

this is an area ripe for research, with little work done to date.

Our work in heuristic pruning and action selection mechanisms (see chapter 5) showed

that we could create different behaviour in an artificial agent without diminishing play

strength. If our approximation of entertainment through complexity holds, then we have

also shown that we can make agent play more entertaining to humans through these modifi-

cations, which has real implications for game AI.

more efficiently, and thus can make better, more interesting decisions (whether the metric

for those decisions be play strength or complexity of play.)

Below we present and summarise our research contribution, and consider their general-

isation. Then we outline a plan for future work based on some of the topics explored in our

work here. It is not our plan to execute all these plans, but we leave them here as a indi-

cation to others who may want to continue this work. Work already under way is some of

that detailed in section 7.3.4, as the application of our techniques to other games has already

provided interesting results.

7.1

Research Contributions