Towards the Automatic Invention of Simple Mixed Reality Games
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Towards the automatic invention of simple mixed reality games Robin Baumgarten, Maria Nika, Jeremy Gow and Simon Colton1 Abstract. The invention of mixed reality games that combine vir- Mixed reality games present a suitable domain for this approach, tual and physical play offers a rich and challenging application area because the computer is already a natural part of the game, and would for AI techniques. We look at the possibility of using descriptive ma- not have to be artificially introduced into play. However, they also chine learning to automatically invent simple mixed reality games. present a challenging domain, partly because the physical data can Specifically, we demonstrate that the HR learning system can gen- be complex and noisy, and partly because of the typical complexity erate coherent domain knowledge from the noisy play data gathered of the game mechanics from a number of simple physical games. We describe how this could We demonstrate here that the HR system [2] is capable of extract- be used to support mixed reality game invention, and discuss the ing sensible domain knowledge from physical play data, obtained prospects for further work in this area. from location tracking of two players engaged in relatively simple physical games, such as Tag (sections 3 and 4). Applying descriptive learning to physical data is an essential first step for the invention of 1 Introduction mixed reality games. We then argue (in section 5) that this knowl- Using AI techniques for game design is not nearly as well researched edge can be used to invent new games, as well as discussing other as using AI for avatars and for non-player characters, even though directions for this work. there is clearly potential to enhance the creativity of game design- ers. We look here at the possibility of using a descriptive learning 2 Background approach to automatically invent simple mixed reality games. Descriptive learning allows interesting concepts and properties 2.1 Descriptive learning & HR of a domain to be discovered from observations, without being re- In a descriptive machine learning setting, an agent attempts to dis- stricted to any particular learning goal. Applied to games, it has the cover a theory that describes a data set. The theory can consist of potential to automatically discover game-specific domain knowledge example objects, concepts which categorise examples, conjectures (rules, strategies etc.) from observed play. This knowledge could help which make claims about concepts, and explanations which support artificial agents fill a number of roles, without the necessity for pro- conjectures. This exploratory behaviour lacks a specific goal, and viding game knowledge to the agent ahead of play. These include: can be contrasted with predictive learning where the goal is to solve a specific categorisation problem. Logic-based descriptive learning Game Player So-called general game playing agents can play un- systems include HR [2], CLAUDIEN [13] and WARMR [7]. seen games without being told game rules or strategies [8]. HR is a theory formation system which generates a theory starting Game Mediator Agents could mediate play between humans, e.g. from an initial collection of example objects, in addition to a set of taking on the role of a referee or coach. initial concepts and a set of axioms which relate the concepts, usually Game Inventor Domain knowledge could be used as a basis for expressed in first-order logic. New concepts are constructed from the constructing new rule sets. existing set using production rules, employing heuristic search based on various measures of interestingness [5] to control exploration of The advantage of using descriptive rather than predictive machine the concept space. HR has 17 production rules which each form a learning (see section 2) is that there is no specific goal, and we can new concept by various syntactic manipulations and combinations of simultaneously find hypotheses describing the environment and the existing concept definitions. The production rules that we used in the particular game being played, which allows a greater understanding application here were: to be developed, e.g. to support game invention. In addition, descriptive learning systems can start with background Compose Conjoins the literals of two input concepts. concepts but no data, and — through the use of third party systems Exists Abstracts ground values to existential variables. such as constraint solvers, model generators and computer algebra Match Unifies distinct variables within a single input concept. systems — can invent concepts and flesh them out with examples Negate Negates literals within a definition. [1]. Such abilities would allow agents to operate in a wider range Size Counts the size of the success set of a clause. of applications, e.g. social environments where humans are creating, Split Instantiates variables in a definition. playing and developing their own games. We envision agents that can Conjectures are formed by HR during the concept search, by ob- join in such social play as a player, mediator or inventor. serving patterns in the sets of known examples that the concepts ap- 1 The Computational Creativity Group, Department of Computing, Imperial ply to. For instance, if HR noticed that the example set of a newly- College London, UK. Contact email: [email protected] formed concept was exactly the same as that of a previously defined concept, it would make an equivalence conjecture stating that the two 3 Mining conjectures from physical data definitions are logically equivalent. Conjectures can be proved from known axioms and theorems either internally or using a third-party We took a three stage approach to generating domain knowledge automated theorem prover: this can add theorems to the theory or, if from observations of physical game playing: the proof is based on a very simple subset of background knowledge, Data gathering Play data was collected from multiple rounds of it can be used to remove trivial conjectures from the theory. several games (section 3.1). Note that in domains where the data may be noisy, HR is able Data encoding Logic based descriptive learning systems, like HR, to make near-equivalences, i.e., equivalence conjectures where the require input in the form of logical statements. For each game, the truth of the conjecture is only partially supported by the data. The play data was encoded as a set of ground first order predicates. user is able to set a parameter for the minimum fidelity of conjectures These predicates were hand-crafted to describe the physical do- (usually in the range 60-80%). For instance, if the user set the value to main (e.g. relative positions in physical space), but are not game- be 75% and HR reported the conjecture that A ↔ B, then this means specific (section 3.2). that, of all examples which satisfy either property A or property B, Descriptive learning We used HR to form a theory about the data at least 75% of them satisfy both properties. The user is also able to in the given encoding. HR’s theory investigation tools to help us specify that the calculation of the fidelity is carried out on only the identify the most interesting conjectures which described the ac- positive examples of the concepts. This tends to avoid the reporting tions of the players in the game (section 3.3). of near-equivalences between concepts for which the examples are mainly negative, for instance the false conjecture in number theory The approach is independent of the games studied here, and could be that the concept of square numbers is equivalent to the concept of generalised to other game domains — providing suitable data encod- prime numbers. While there is no overlap in the positive examples ings can be designed. of these concepts, the sparsity of examples on the number line mean that this conjecture has 65% fidelity over the numbers 1 to 100. As mentioned above, HR’s search is driven by heuristic measures 3.1 Data gathering of interestingness. These measures can also be used to filter and sort the concepts and conjectures in HR’s output. The two measures we The Ubisense location tracking system [16] was installed in a use here are applicability and fidelity of conjectures. Applicability is medium sized room (approx. 10m by 6m). Each player carried a defined as the number of examples that a conjecture relates. Hence, tracking ‘tag’ with a single button which they could use to provide conjecture about even prime numbers score very low for applicabil- additional play data (see Figure 1). To increase the accuracy of the ity, as they only describe the number 2. Fidelity is measured as the location tracking, the players walked rather than ran, and players re- proportion of examples that support a conjecture, for instance the mained in sight of the location sensors. Because of these artificial conjecture that prime numbers are odd, while false, scores highly for constraints, the games were more simulated than played, although fidelity, because it is nearly true — with only one exception. they were still engaging physical activities for the players involved. HR has been applied to a variety of domains, most notably math- A more sophisticated approach (e.g. with better tracking technology) ematical domains where it has been used to make some interesting might remove these artificial constraints. discoveries [6]. Of particular relevance to mixed reality games is the We chose three simple physical games, plus one structured physi- extension of HR to work with noisy data in order to learn about the cal activity: rules of a dice game from vision data [14]. Tag One player attempts to catch the other, and when caught they swap roles. Both players constantly clicked their button to indicate they were still more than one metre apart.