# Game Play Schemas: From Player Analysis to  Adaptive Game Mechanics

Authors: Craig A. Lindley; Charlotte C. Sennersten
Journal: International Journal of Computer Games Technology
 (2008)

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## Abstract

Schema theory provides a foundation for the analysis of game play patterns created by players during their interaction with a game. Schema models derived from the analysis of play provide a rich explanatory framework for the cognitive processes underlying game play, as well as detailed hypotheses for the hierarchical structure of pleasures and rewards motivating players. Game engagement is accounted for as a process of schema selection or development, while immersion is explained in terms of levels of attentional demand in schema execution. However, schemas may not only be used to describe play, but might be used actively as cognitive models within a game engine. Predesigned schema models are knowledge representations constituting anticipated or desired learned cognitive outcomes of play. Automated analysis of player schemas and comparison with predesigned target schemas can provide a foundation for a game engine adapting or tuning game mechanics to achieve specific effects of engagement, immersion, and cognitive skill acquisition by players. Hence, schema models may enhance the play experience as well as provide a foundation for achieving explicitly represented pedagogical or therapeutic functions of games.


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## Body

## 1. Introduction

Computer game genres,  such as role-playing games (RPGs) and
first-person shooters (FPSs),  imply particular sets of design features
supporting expectations that prospective players have about the nature of the
play experience that games support, based upon past experiences with other
games in the same genres. When a player first encounters a computer game within
an unfamiliar genre, they will, if sufficiently motivated, interact with the
game and eventually learn sufficient patterns of interaction to make progress
within the game, perhaps eventually completing it. Game play is therefore
fundamentally a process of players learning, adapting and improving play
skills. Since computer games are predominantly played by the use of very
generic interaction technologies (e.g., a keyboard and mouse), learning and
adaptation in play are, for the most part, processes of developing cognitive
skills focused upon the mechanics of a game and its media realization, based
upon an existing general skill set for computer use. Keyboard and mouse
operations are mapped onto in-game actions in a game world synthesized by the
game software. Learning how to play can therefore be divided into three phases:
(1) learninginteraction mechanics, that is, the basic motor operations
required to operate, for example, a keyboard and mouse in a largely unconscious
way; (2) learning  interaction semantics, that is, the simple associative
mappings from keyboard and mouse operations to in-game actions (and meta-game
actions, such as setting play options, or loading and saving game states); and (3)
learning game play competence, that is, how to select and perform in-game actions in the
context of a current game state in a way that supports progress within a game.
Interaction semantics represent a basic level of competence in playing a
particular game; these mappings are often carried across different games within
a genre and even across genres (e.g., using “w,” “a,” “s,” and “d” keys to move
a player character forwards,
left, backwards and right, resp.). Learning interaction semantics
represents a form of game challenge (in addition to those noted by Rollings and
Adams [1]), but once the basic mappings have been learned, they become a
largely unconscious foundation for ongoing game play. The focus of learning
then shifts to the development of game play competence, which involves the
development of forms of in-game situation awareness and decision making needed
to meet the more complex challenges such as those documented by Rollings and
Adams [1].Game play competence involves the ability to (1) decode the
audiovisual sensory and perceptual information delivered by the game media
(e.g., the computer screen and speakers) into the apprehension of a local
situation within the synthesized game world (or game space); (2) evaluate this
understanding of the local in-game situation in terms of the overall objectives
of play, current goals and tasks, the state of the player character within the
game (e.g., capabilities, health, and other statistics), and anticipation of
various rewards of playing the game; (3) make decisions about which in-game
tactics and action(s) to perform next, based upon the perceived situation and
its evaluation; and (4) perform action(s) based upon competence in interaction
mechanics and semantics. The details of the cognitive process underlying this
repetitive sequence, which could be described as the
sense->model->evaluate->plan->act sequence (essentially the same as
the sense->model->plan->act structure used to simulate higher-level
action control in robots and agents within artificial intelligence research;
seehttp://www.cgie2006.murdoch.edu.au/game_ai.html
for extensive references), are the primary higher-level cognitive
learning outcome of learning how to play a particular computer game.The general usefulness of these different aspects of
learning in game play relates to the degree to which the knowledge or skills
learned may transfer to other contexts. Competence in interaction mechanics is
very general, transferring to all contexts within which the same interaction
technologies are used; however, the contribution of a particular game to the
development of this competence is likely to be very limited, and certainly no
greater than other applications using the same interface technology. In fact, a
game may be less effective than other applications that are more demanding in
terms of knowledge, for example, of keyboard layout, such as word processors.
Competence in interaction semantics transfers only to other systems using the
same mappings from mechanical interaction operations to in-game actions. This
may include many other games, especially those within the same genre but also
across genres, depending upon their adoption of implicit or explicit
conventions in game-interaction
design. However, interaction semantics may be limited in their transferability
to other contexts, since contemporary methods of triggering synthetic actions
synthesized by a computer game are unlikely to be the same as methods of realizing
actions that are not synthesized by a computer.Game play competence has similar transferability across
computer games to competence in interaction semantics, that is, high
transferability within a genre but decreasing across genres. However, the
potential for transfer of game play competence to contexts other than computer
games may be much greater, since similar cognitive processes implementing a
sense->model->evaluate->plan->act sequence could apply within those
contexts. For example, a flight simulator based upon accurately modeled flight
planning and air traffic control procedures may help players to learn how to
manage flight planning and air traffic control operations in a real flying
context. The key issue here is whether the particular mechanics and design
features of the game lead to the development of cognitive structures that can
transfer to other contexts. The effectiveness of computer games as situated
learning environments (as characterized by [2]) critically depends upon this
issue of transfer.The nature of the cognitive structures underlying game play
is not only relevant to knowledge and skill transfer. Those structures are the
key to therapeutic applications of game play (e.g., [3]), and in fact are the
key to the ability of computer games in all contexts to engage and immerse
players and motivate ongoing play. This follows since it is the game play
schema driving the situated decision process that determines the nature and
timing of emotional rewards motivating play. Hence, a greater understanding of
the cognitive structures underlying game play and how motivations and rewards
are related to these can aid in better game design in entertainment, pedagogy,
and therapy. More than this, it is the central claim in this paper that
explicitly modeling those cognitive structures and processes within a computer
game engine has the potential to greatly enhance design effectiveness by
providing the foundations for the game system itself to guide the development of cognitive
structures and control the emotional rewards underlying play.This
paper explores this issue by first considering the cognitive framework for
analyzing game play described by Lindley and Sennersten [4]. Methods for
conducting analyses of play with a view to identify underlying game play
schemas are then described. Based upon this, the paper goes on to consider
potential methods by which a computer game system might itself form hypotheses
about the schemas underlying the play of a particular game. Finally, we
consider some ways in which hypothesized game-play schemas can be used
automatically within a computer game system to modify game mechanics as a basis
for guiding play and influencing ongoing schema formation and refinement on the
part of a player. This work differs significantly from many past projects to
create computational players (e.g., see [5]) in that the latter are typically
focused on optimal game-play methods that do not need to use computational
techniques based upon human play performance. In the case of the work described
here, the particular strength and interest of the method are the
characterization and explicit representation of the specific algorithmic
strategies and cognitive processes of human players, both for analytical
purposes and as a foundation for adaptive game mechanics.


## 2. A Cognitive Theory of Game Play: Tasks, Attention, Schemas, and the Pleasures of Play

Lindley and Sennersten [4] present a theory of the
underlying cognitive systems involved in game play based upon schema theory and
attention theory. Schemas are cognitive structures that link declarative (or
factual) and procedural (or performative) knowledge together in patterns that
facilitate comprehension and the manifestation of appropriate actions within a
context. While the taxonomical structures of semantic or declarative memory are
comprised of object classes together with associated features and arranged in
subclass/superclass hierarchies, the elements of schemas are associated by
observed contiguity, sequencing, and grouping in space and/or time [6]. Schemas
can refer to declarative knowledge and taxonomical types with their features
and relationships, and integrate these with decision processes. Schemas include scripts for the understanding and enacting of behavioral patterns and
routines, a classic example being Schank and Abelson’s [7] example of the restaurant
script that includes a structure of elements for entering a restaurant,
sitting down, ordering food, eating, conversing, paying the bill, leaving, and
so on.  Scripts, as structures used for both comprehension and behavior
generation, represent a structure of cognitive functions that may include
cognitive resources, perceptual interpretations and preconditions, decision
processes, attention management, and responsive motor actions. Story schemas are patterns representing a structure of understandable elements that must
occur to make stories comprehensible. The presence of story schemas in the
cognitive systems of storytellers, listeners, readers, or viewers of stories
allows stories to be told and to be comprehended, including the inference of
missing information. If a story deviates too far from a known schema, it will
not be perceived as a coherent story. Script and story schemas are concerned
with structures of both space and time, while scenes are schemas
representing spatial structures, such as the layout of a house, a picture or an
area of a city.While schemas have been interpreted in many different ways,
here agame play schema is understood as a cognitive structure for
orchestrating the various cognitive resources required to generate motor
outputs of game play in response to the ongoing perception of an unfolding
game. A game play schema is therefore the structure and algorithm determining
the management of attentional and other cognitive, perceptual, and motor
resources required to realize the tasks involved in game play. Examples of
types of game-play schemas described by Lindley and Sennersten [4] include
story scripts for understanding high level narrative structures designed into games,
and scripts for the combative
engagement of an enemy, exploring a labyrinth, interacting with a trader nonplayer
character, and negotiating and carrying out quests.Attention theory provides an account of the energetic
resources available to cognition, together with principles for the distribution
of energy (or attention) to the cognitive resources that use (or manifest) it.
Attention theory addresses issues of attentional focus, management of attention
(including attentional selection), and the allocation of cognitive resources to
cognitive tasks. Ongoing research is addressing the question of the detailed
operation of attentional mechanisms, including questions such as the degree to
which attentional capacity is specific to specific cognitive resources (or
modes) or sharable among resources according to demand, and the stage of
processing of perceptual information at which perceptual information is
selected for attentional priority. Schemas can be regarded as mechanisms or
algorithms that, among other functions, determine the allocation of attention
to cognitive tasks.In the context of game play, attention and the operation of
game play schemas are driven by hierarchical goals that set tasks for a player.
Goals include those intended by designers and those created by players as
allowed by a game design. A hierarchical decomposition of game play goals might
at a high level include the completion of a game, which decomposes into the
subgoals of finishing each of its levels, each of which in turn decomposes into
goals of completing a series of game challenges (and other tasks invented by
the player).We hypothesize that this hierarchical goal structure is
mirrored in a hierarchical structure of schemas within a player’s cognitive
system, where a schema is an algorithm for completing a particular goal or
subgoal. As argued by Lindley and Sennersten [4], this schema structure is
fundamental to many aspects of the pleasures and motivating factors behind
play. These include the pleasures of the following.
(i)
Effectance which is a basic feeling of empowerment created when an action of a player
results in a response from the game system [8]. The cause-effect relationships
underlying effectance are a fundamental premise of goal-oriented schemas for
action.(ii)
Closures at different hierarchical levels (as described by Holopainen and Meyers [9]),
where a closure is interpreted here as the completion of the algorithm
constituted by a play schema. Closures may involve completion of expected
outcomes and resolution of dramatic tensions, corresponding to the completion
of cycles of suspense and relief identified by Klimmt [8]. A distinction must
be made here between the intrinsic pleasures of schema completion and more
complex emotional experience and rewards due to fictional identification within
the game world (see the point below regarding episodes).(iii)
Achievement of in-game tasks which is rewarding due to the displacement of a
player’s identity into their character [9], this being a matter of imaginative
immersion as described by Ermi and Mäyrä [10]. Achievement-oriented reward is a more specific form of reward than mere closure, since it is associated
with the completion of schemas by the achievement of specific goals.(vi)
More complex forms of enjoyment in game tasks regarded asepisodes [8] following from imaginative displacement into the game world. Enjoyment within episodes may include the excitement of possible action, the pleasures of curiosity and discovery, the pleasures of experiencing negative emotions of suspense followed by the transference of arousal to an ecstatic experience when the challenge creating the anxiety of suspense is overcome, and enhanced self-esteem. Schemas offer greater discrimination of the pleasures involved in episodes by allowing different forms of episodes to be modeled as different schema patterns having a complex substructure with corresponding emotional effects (e.g., different scripts for solving mysteries, combat, exploration, trading, and quest negotiation).(v)
Escape to an alternative reality provided by the fictional world represented by a game
[8] and facilitated by imaginative displacement. Players have the pleasure of
being able to experience new objects, actions, social interactions, and experiences
at no risk. These vicarious experiences can help players to cope with felt
frustrations and deficiencies in their everyday lives, a process both of
catharsis and of perception of increased competence and relevance. Schemas for
stories facilitate displacement, while many additional schema forms provide the
foundations for comprehension of the events within the fictional world and
provide mechanisms for projection of the player’s sense of self into the
fiction.(vi)
Achievement of a sense offlow [11] in
game play, this being a state at the boundaries between engagement and
immersion, of being totally absorbed in meeting a constantly unfolding challenge.
We hypothesize that the flow state is associated with attentional demand, in
particular occurring when schema execution demands attentional resources above
a level that would result in player boredom and below a level that would result
in excessive difficulty and consequent frustration.
Schema theory therefore has the potential to provide both an
explanation of the decision and operational processes underlying game play and
an explanation of the detailed reward and motivation factors behind play.
Validating this potential requires detailed study of play resulting in the
development of empirically validated hypotheses about the detailed structure
and functionality of game-play schemas, for individual players and across
groups of players.


## 3. Methodologies for Identifying Game Play Schemas

Identification of game play schemas is a knowledge
acquisition and representation process. Our current methodology for doing this
includes analysis of the design features of test games, logging of player key
strokes and mouse movements, recording of the screen history of play,
eyetracking data showing the locus and dynamics of player gaze behavior, and
think-aloud protocols to gain some insight into the player’s conscious
experience of play and its decision processes. Analysis of this data then
proceeds by a process of detailed analysis of individual play sessions in order
to identify different play modes and abstract hypothetical underlying game play
schemas. This in itself is a complex process that may begin with cognitive task
analysis (CTA, seehttp://mentalmodels.mitre.org/cog_eng/ce_methods_I.htm),
but must end with a detailed cognitive explanation of the decision processes
involved in terms of basic cognitive functions. Statistical patterns of play
interaction (mouse moves, key strokes,  and eye movements) that may correlate
with the presence and execution of specific game play schemas are then
identified. This requires the separation of an analysis dataset from which
schema models and initial statistical distributions are derived from a test
dataset that can then be used to validate those schema models. This sequence is
iterated in order to refine the identified schema models.The design features of the games used within these studies
are crucial. Hence, an initial analysis of the selected games must be made in
order to identify their general features. The iterative process of refining and
validating hypothetical game play schemas must also involve the creation of
purpose-specific test games or levels, this being done by level editing and modding
(i.e., modification of off-the-shelf games, potentially including their media
content and scripted behavior). It is also possible to implement a hypothetical
schema to create a computational player and to test the resulting game play
interactions with actual player interaction as another method of validating a
schema hypothesis. As noted by Lindley and Sennersten [4], a CTA provides the first
approximation description of a game play schema, but a CTA is also heavily
determined by the language and cultural constructs of the observer. The
phenomenologically meaningful terms of a CTA may have to be further analyzed to
account for the ways that those high-level constructs are actually realized by
underlying neurophysiological mechanisms, and this mapping could involve
different parsings of functional units at the CTA and neurophysiological
levels. Hence, a game play schema might be described at different levels of
abstraction or from different interpretation perspectives, some being
meaningful in terms of the subjective languages of task performance (e.g., the
terms of self-reported task performance) or CTA and others in terms of
implementational neurophysiology that may have a very different structure and
functional decomposition than that of more linguistically conditioned accounts.The choice of the level of abstraction in game play schema
descriptions may depend upon the purpose of the analysis. More importantly,
however, it may be that distinctive statistical profiles can be associated with
schema characterizations at an optimal level of abstraction; more abstract
schema descriptions may be too general to have any statistical discrimination
between them, while more detailed descriptions may involve details that cannot
be correlated with statistical groups. Hence, an important ongoing task is the
statistical validation of suitable levels of schema description. It is yet to
be determined how consistent the level of description needs to be, across game
genres, games within genres, different kinds of players, different players
within those types, and different play sessions for the same player. It is
hoped, however, that applying this methodology will result in statistical
profiles uniquely associating player types and game design feature sets with
distinctive statistical distributions of interaction primitives at the level of
interaction semantics that indicate specific hypothetical game play schemas (or
sets of schemas) within the cognitive systems of those players. This is a large
undertaking (and in fact endless, as game design continues to evolve) that must
be approached incrementally by focusing upon specific genres, games, and design
feature subsets.Questions of levels of abstraction and also of higher-level
structures also apply to interaction primitives. Basic interactions implement
game moves at the semantic level. However, the presence of specific schemas may
be indicated by specific sequences or clusters of interaction semantics, rather
than, or in combination with, their frequency. Different play modes, such as
setting options versus game play commands directed towards achieving in-game
goals (i.e., game moves) can often be distinguished by specific discrete
interaction primitives, such as hitting an escape key. However, manual
interpretation of play and the formation of schema hypotheses based upon this are
crucial for defining criteria for distinguishing between the presence of
different schemas that involve the same or similar interaction primitives.To illustrate this discussion, a hypothetical schema can be
described based upon observations of play of the role playing gameNeverwinter
Nights. Neverwinter Nights is a third-person point of view game in
which the player has a primary in-game character and this character can gain a
number of companions in order to form a team, also controlled by the player.
Within the game world there are many underground labyrinths consisting of rooms
and chambers connected by passages. Rooms and passages often have doors and the
labyrinths in general contain threats such as monsters and traps, nonplayer
characters that may be friendly or hostile depending upon how the player
character interacts with them, powerups and various treasures. The play
patterns observed for this example occurred within a period of the game during
which the player is intended (by the game designers) to be acting to achieve a number of higher-level
story goals predesigned into the game; in particular, the player is on a quest
to find four specific creatures that are the key to creat an antidote for a
plague and each of which is hidden somewhere within its own labyrinth. Each
labyrinth has a similar abstract structure and distribution of game challenges,
with differences in its thematic realization. This leads to a style of game
play that manifests highly repetitive patterns of interaction and decision making.
The schema, expressed in this case in a kind of high-level and informal
pseudocode, is a hypothesis about (part of) the underlying algorithm
responsible for manifesting these repetitive patterns as the player character
and team move through the labyrinths.The question of the level of abstraction involved is illustrated by considering a
significant number of possible subtasks and additional tasks that are not
represented in the above description:  Check
health bar for1,…,N characters, Check for treasure/items to
pick up, Check item attributes/quest relevance, Select
navigation waypoints for movement, Avoid enemies during retreat,
Tweak group member positions, Bring back strays,
Check status of quests, Talk with NPCs, Accept/reject quests,
Check minimap window, Reconfigure inventory, Reconfigure equipped
items, Select level up options, and so forth.A complete schema description must
include all possible subschemas and include a way of representing the operation
of simultaneous parallel schemas, their relative priority, and the principles
for switching from one schema to another. The detail involved can be high. For
example, the detailed description of a subtask such as “check map window” must
include an account of exactly what it is that is being looked for in the map
window, how the data is to be interpreted, and some kind of representation for
the outcome of the minimap check (e.g., a decision about being lost and/or
activating a goal-related reorientation subschema).


## 4. Automated Identification of Game Play Schemas and Schema-Based Adaptation of Game Mechanics

The intended outcome of schema analysis over significant
numbers of players and play sessions is a probabilistic profile of the
frequencies, clusters, and/or sequences of semantic interaction primitives
(game moves) associated with different types of underlying game play schemas
for a specific game (i.e., its design features). If such a set of statistical
profiles is available, it may be possible to use the profiles for automated
identification of the schemas of particular players/play sessions. It is possible to
automatically record (or log) interaction semantics for a particular player
during a particular play session or across different play sessions. This will
result in a count of the absolute frequency of each type of semantic
interaction primitive used by that player, which can be turned into a relative
frequency by subdivision with the total count of interaction primitives. This
might be used (in addition to specific commands that indicate changes in play
mode) to match against a database of statistical profiles of different game
play schemas in order to derive a probabilistic hypothesis about the likelihood
that specific known schemas are underlying play. Here, we hypothesize that poor
overall correlations are likely to indicate the presence of previously
uncharacterized schemas, which ideally should be returned to a central schema
repository for the game for analysis and new schema description development and
distribution.As described above, game play schemas represent the
significant learning outcomes of game play and also encapsulate various rewards
of play. An explicit representation of desired and observed game play schemas
within a game system constitutes a knowledge base that can potentially be used
automatically for a variety of purposes by the game system.Schema representation and mapping can be used for automated
monitoring of a game design as a method of validating the design in terms of
player satisfaction. Since a schema includes various points of player reward
and presents a time structure for the emotional experience of a game, the
schema indicates a hypothesis about the nature of the emotional experience of
the player. Different players may prefer different forms of satisfaction.
Monitoring schemas and schema execution may indicate which forms of
satisfaction particular players are seeking. It may also provide a foundation
for determining when a player is not achieving enough satisfaction (based upon
criteria that may be derived from a player’s history of play, since different
players may have different demands in terms of the nature and intensity of
rewards). This may be because a game is too easy or too difficult, in which
case the game mechanics and parameters determining difficulty for a specific
schema can be modified to achieve a better subjective experience. It may also
be that a player has not discovered those elements of a schema needed in order
for its execution to result in a satisfying experience, in which case the game
system model of the player schema related to a target schema might be used to
change the game mechanics, for example, to dynamically adapt a level design or
to introduce instructional material (perhaps by spawning a suitably informed
NPC) to lead the player to actions (such as going to a specific training
scenario) that result in gaining the appropriate skills. In effect, this can
amount to more efficient and dynamic use of in-game tutorials together with an
automation of the normal processes of game tuning carried out manually during
game testing prior to release, but having the advantage of being tuned to
specific players rather than a group of commercial testers.Schema descriptions can also be used to explore the
effectiveness of a game design in realizing designers’ intentions. Simple
observations may indicate basic design failures, such as the visual design of
interactive elements leading them to be too unobtrusive within the game space
for players to notice. However, schema descriptions may show deeper and less
obvious problems, such as design features leading too soon to limited modes of
play that reward players too much for play patterns that are developed very
quickly, discouraging them from exploring a game enough to discover other
aspects of its mechanics. As with the other examples presented here, actively
using these models within a game system allows the mechanics to be varied for
individual players, instead of providing a single solution that is supposed to
accommodate everyone.Monitoring schema formation can also result in automated
detection of the degree to which a game design is achievingemergent game
play (see [12]) where the design rather loosely constrains the nature of
the play experience. In this case, poor correlations with known schemas may be
a positive indicator of emergent play. Conversely, design features may be
selected that are compatible with a broad set of known schemas representing
very different play styles, ensuring that a design accommodates a wide variety
of play styles, a space within which players have a lot of freedom to create
patterns of interaction.A major use of explicit schema representations may lay in
pedagogical or therapeutic functions of game play. In this case, target schemas
may not be initially derived from game play but from the target application
domains for learning or training. For example, in a military application,
observation of tactical decision making in the field could support the
development of schema descriptions for tactical decision making. A game for
tactical training should then encourage players to preferentially develop the
same or, functionally, similar schemas. The effectiveness of the design of a
game intended for tactical training can then be assessed by comparing the
schemas of players derived from observation of their play patterns with those
of operational tacticians. This may be a great advantage compared to assessing
performance outcomes, since performance outcomes alone only indicate how a
player has mastered a game system, with no indication of how well the mastery
of the game will transfer to an application domain. The schema description is
an explicit representation of the cognitive capabilities that facilitate
operational competence, thereby having much greater transfer potential from the
game to the target application environment. Moreover, aspects of the
operational schemas that cannot be facilitated by game design provide an
explicit representation of the limits of transfer that may then be used to
appropriately focus on supplementary training.Just as in the case of tuning game mechanics for player
satisfaction, explicit schema representations and monitoring of player schemas
can be used to adapt game mechanics to achieve pedagogical or therapeutic
outcomes. For example, a game designed to train players to achieve batter
spatial navigation skills might present an initial diagnostic level involving a
comparatively complex navigation task based upon a variety of cues, such as
verbal descriptions, minimaps, distance cues, and local cues like footprints
and vehicle tracks. Based upon which cues players use, ongoing levels can
reduce or exclude cues that are already taken into account and emphasise
neglected cues to encourage the development of broader attention patterns.


## 5. Conclusion

This paper has described an approach to the analysis of game
play based upon schema theory and attention theory. An empirically based method
has been described as a basis for identifying and validating hypothetical game
play schemas. Automated schema recognition and the potential uses of explicit
schema representations within game systems have been explored. This approach
provides for explicit modeling of the cognitive systems and processes
underlying game play, both for analytical studies of play and as a potential
implementation mechanism for adaptive games. Work on the analysis of games
using this approach is ongoing. It is hoped that the results of this work will
provide the foundations for future implementation of schema-based adaptive game
systems.Algorithm 1
1. Stop at Closed Door2. Check health of partyif>1 party member low, then:Rest PartyResummon summoned creatureelseif 1 party member low, then:if lots of healing potions, then:administer healingelseRest PartyResummon summoned creature3. Enter combat configuration4. Open door and enter room5. If there is an enemySelect targetMonitor health of party until enemy defeatedif>1 party member has low health, then:Run awayRest PartyResummon summoned creatureGo back to step 3elseif 1 party member low, then:if lots of healing potions, then:administer healingelseRun awayRest PartyResummon summoned creatureGo back to step 36. If enemy remains, go to step 57. Check for traps…… etc. …



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*Source: 216784-2008-02-04.xml*