Game Development Reference
In-Depth Information
Validation o
f
player output
Play
e
r challenges
Helper artifacts
Discovery
Automated exact
Competition
Automatic approximative
Self-challenge
Bootstrapping
Online player agreement
Social experience
Offline player agreement
0
5
10
15
20
25
30
0
5
10
15
20
25
30
Purpos
e
encapsulation
Task
difficulty
High
Gradually cmplx tasks
Medium
Equally cmplx tasks
Low
0
5
10
15
20
25
30
0
5
10
15
20
25
30
Task dis
t
ribution
Anti-ch
e
ating measures
Player capability driven
N/A
Data (ontology) driven
A posterior
Task-value driven
Anomaly detection
Mutual supervision
Greedy
Restrictive rules
Random
0
5
10
15
20
25
30
0
5
10
15
20
25
30
Fig. 7.2
SAG design dimensions—number of SAGs per each pattern
The Fig.
7.3
shows us that discovery as a game aesthetics is used only sparsely
for SAGs. Yet, we were quite strict in associating of this “pattern” to SAGs: we
only counted SAGs, authors of which explicitly “declare” they entertain players by
their content (in fact, these were only our PexAce and CityLights). However, if we
admit that, for example, all “multimedia-related” SAGs to some extent entertain
players by new multimedia content, the discovery columns of the table would
be more populous. It may be viable for the future development of multimedia
metadata acquisition games, to consider relying more on the discovery aesthetics,
by adaptive selection of content potentially attractive to players.
As can be seen in the Fig.
7.3
, the two “secondary” dimensions (anti-cheating mea-
sures and task distribution) further concretize the allocation of existing SAGs. We
can, for example, we may observe a relatively low utilization of mutual player super-
vision (as anti-cheating measure) in multiplayer (online player agreement) games.
This is because these games are in majority cooperative, not competitive on the ses-
sion level. On the other hand, other anti-cheating measures such as restrictive rules
and anomaly detection are used with all types of games. Considering the task distri-
bution, there is a overall dominance of greedy approach (as this is natural and easy to
implement) seconded by task-value driven approaches, followed by data- and player
capability-driven approaches. Yet, no apparent differences in SAG counts can be
observed regarding other dimensions. The only one we noticed, was a slightly higher
use of data-driven task assignment for single player games.
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