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A.M. Mora, F. Aisa, R. Caballero, P. García-Sánchez, P.A. Castillo, J.J. Merelo, P. De las Cuevas
• Unreal (game, environment)
• Pogamut tool
• Unreal Expert Bot:
• objectives, memory, FSM, workflow
• Experiments and results
• E-Bot vs Standad Bots, E-Bot vs Human players
• Conclusions and future work
Unreal is a first person shooter (FPS).
Famous due to the excelent AI of the enemies (bots), which makes it an
amazing multiplayer game. Unreal Tournament series is widely extended.
It offers an editor (UnrealEd) which lets us change almost anything in
the game even the behaviour of the bots. It uses the language
UnrealScript.
A java middleware for Unreal Tournament series games and
Defcon games.
The architecture is as follows:
It is possible to interact with the game from a java program,
getting higher independence (avoiding Unrealscript restrictions)
and increasing the possibilities (java libraries).
On the contrary, the structures, classes, functions and
workflows defined in the Unreal engine, cannot be accessed, nor
used.
• Create an autonomous agent for playing Unreal
Tournament deathmatch championship.
• Considering the constraints of this competition:
- 1 vs 1 matches
- Small arenas
- Weapons are not respawned
- Some forbidden items (U-Damage, for instance)
- 15 minutes per match instead of a number of frags (kills)
• Human-like behaviour is desired.
• Modelling Expert player knowledge (and tricks).
- High control in timing (items respawn time)
- Deep knowledge about weapons and their advantages and
disadvantages
- Deep knowledge about items
• There is used a Database which models the bot’s memory.
• It is uploaded with data about locations of items and
weapons in the map, but just once the bot has seen them.
• It cannot store lots of information because it would be
unmanageable in real-time for the bots.
• Data:
• Unreal_ID  unique identification of an item in Pogamut
• Type  health, shield, ammo, weapon, ...
• Name  name of the item or weapon
• Map  name of the map where it is placed
• Position  location of the item (extracted from the ID)
• Its behaviour is defined by means of a Finite State Machine
based AI with two state levels.
Modelling Human Expert Behaviour in an Unreal Tournament 2004 Bot
Modelling Human Expert Behaviour in an Unreal Tournament 2004 Bot
• Expert Bot (E-Bot) fought against standard game bots in the
maximum difficulty level.
• This difficulty level is quite hard for a medium level
player.
• E-Bot outperformed the standard bots in the game (considering
the number of frags), in two different maps and three
different matches.
• Expert Bot (E-Bot) also fought versus four different medium-
level human players. They play sometimes but they are not
‘professionals’.
• E-Bot is hard to beat for humans, even for our expert, but he
clearly won all the matches against the bot.
• We have modelled the knowledge of a human expert player in
the game Unreal Tournament 2K4, by means of a two-level FSM,
a database (memory) and a wide set of rules.
• This human-like Expert Bot (E-Bot) outperforms the standard
Unreal Tournament 2K4 bots in the hardest difficulty.
• It is also a hard rival against human players.
FUTURE WORK
• Improve the bot’s movement which is far for being perfect:
the bot doesn’t jump (due to Pogamut flaws).
• Increase the memory (database) complexity and usefulness.
• Apply any computational intelligence method for improve the
FSM or the set of rules, Evolutionary Computation methods for
instance.
Contact: amorag@geneura.ugr.es
Source Code: https://github.com/franaisa/ExpertAgent

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Modelling Human Expert Behaviour in an Unreal Tournament 2004 Bot

  • 1. A.M. Mora, F. Aisa, R. Caballero, P. García-Sánchez, P.A. Castillo, J.J. Merelo, P. De las Cuevas
  • 2. • Unreal (game, environment) • Pogamut tool • Unreal Expert Bot: • objectives, memory, FSM, workflow • Experiments and results • E-Bot vs Standad Bots, E-Bot vs Human players • Conclusions and future work
  • 3. Unreal is a first person shooter (FPS). Famous due to the excelent AI of the enemies (bots), which makes it an amazing multiplayer game. Unreal Tournament series is widely extended. It offers an editor (UnrealEd) which lets us change almost anything in the game even the behaviour of the bots. It uses the language UnrealScript.
  • 4. A java middleware for Unreal Tournament series games and Defcon games. The architecture is as follows: It is possible to interact with the game from a java program, getting higher independence (avoiding Unrealscript restrictions) and increasing the possibilities (java libraries). On the contrary, the structures, classes, functions and workflows defined in the Unreal engine, cannot be accessed, nor used.
  • 5. • Create an autonomous agent for playing Unreal Tournament deathmatch championship. • Considering the constraints of this competition: - 1 vs 1 matches - Small arenas - Weapons are not respawned - Some forbidden items (U-Damage, for instance) - 15 minutes per match instead of a number of frags (kills) • Human-like behaviour is desired. • Modelling Expert player knowledge (and tricks). - High control in timing (items respawn time) - Deep knowledge about weapons and their advantages and disadvantages - Deep knowledge about items
  • 6. • There is used a Database which models the bot’s memory. • It is uploaded with data about locations of items and weapons in the map, but just once the bot has seen them. • It cannot store lots of information because it would be unmanageable in real-time for the bots. • Data: • Unreal_ID  unique identification of an item in Pogamut • Type  health, shield, ammo, weapon, ... • Name  name of the item or weapon • Map  name of the map where it is placed • Position  location of the item (extracted from the ID)
  • 7. • Its behaviour is defined by means of a Finite State Machine based AI with two state levels.
  • 10. • Expert Bot (E-Bot) fought against standard game bots in the maximum difficulty level. • This difficulty level is quite hard for a medium level player. • E-Bot outperformed the standard bots in the game (considering the number of frags), in two different maps and three different matches.
  • 11. • Expert Bot (E-Bot) also fought versus four different medium- level human players. They play sometimes but they are not ‘professionals’. • E-Bot is hard to beat for humans, even for our expert, but he clearly won all the matches against the bot.
  • 12. • We have modelled the knowledge of a human expert player in the game Unreal Tournament 2K4, by means of a two-level FSM, a database (memory) and a wide set of rules. • This human-like Expert Bot (E-Bot) outperforms the standard Unreal Tournament 2K4 bots in the hardest difficulty. • It is also a hard rival against human players. FUTURE WORK • Improve the bot’s movement which is far for being perfect: the bot doesn’t jump (due to Pogamut flaws). • Increase the memory (database) complexity and usefulness. • Apply any computational intelligence method for improve the FSM or the set of rules, Evolutionary Computation methods for instance.
  • 13. Contact: amorag@geneura.ugr.es Source Code: https://github.com/franaisa/ExpertAgent