This paper presents a deep description of the design of an autonomous agent (bot) for playing 1 vs. 1 dead match mode in the first person shooter Unreal Tournament 2004 (UT2K4). The bot models most of the behaviour (actions and tricks) of an expert human player in this mode, who has participated in international UT2K4 championships. The Artificial Intelligence engine is based on two levels of states, and it relies on an auxiliary database for learning about the fighting arena. Thus, it will store weapons and items locations once the player has discovered them, as a human player could do. This so-called expert bot yields excellent results, beating the game default bots in the hardest difficulty, and even being a very hard opponent for the human players (including the expert). It has been presented at SEED 2013 (I Simposio de Entretenimiento Digital) in CEDI2013.

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