- 1 Project Progess
- 2 About Me
- 3 Overview
- 4 Rulebases - Formula AI
- 5 Adaptation and Learning - C++
- 6 Related Papers
I am currently a full-time graduate student living in Colorado, US. I've been coding since I was about 12, initially writing text based adventures for myself and my friends to play, moving on to RPGs using some primitive sprite based graphics and completed a mini RPG in high school, complete with graphics and music I wrote on my keyboard.
Since then, I have graduated from Pennsylvania State University with a bachelors degree in computer science and entered a PhD graduate program focusing on AI research. I use C++ primarily for research and have been a teaching assistant for our undergraduate C++ course. I have approximately 4/5 years of C++ experience and some Python experience.
- IRC: barbarianhero
- Forum id: rende
- Gna! username: dhains
- preferred email: dhains__A!T__ gmail.com
I'll be adding more to this page as I have the time. Please feel free to contact me if you have any questions.
Since I have been active with Wesnoth development for the past few weeks, I have submitted several patches and gained SVN access. Since then, I have been focusing on getting Formula AI to a point where I can begin development of the ideas set forth in this proposal by implementing necessary features:
These checkins exposed map and terrain features to Formula AI, a much needed feature. Terrain information is now available to Formula AI using 'map.terrain' from within Formula AI. This allowed implementation of the 'woodchopper' feature requested on the forums.
Formula AI Scripts
The overall goal of the following checkins was to create and support a new way of writing Formula AI, i.e. formula AI script files to allow reuse and support building of pluggable Formula libraries:
- Created def keyword for custom functions: 
- Formula Script support: 
- Cleaned up Formula AI script read method : 
- Fixed Parsing Bug in Formula AI : 
- Added Comment Support to Formula AI : 
- Checked in Vim Syntax Highlighting: 
Formula AI scripts are now supported. Documentation can be found on the FormulaAI page.
Candidate moves and Eval functions
The following checkins are to support the building of candidate move lists and evaluation functions (work in progress):
Various bug fixes: 
Designing an AI for playing games is a challenge. Techniques such as minimax and alpha-beta pruning are useful in games such as chess or backgammon, but the complexity of Wesnoth's gamestate space precludes many of these approaches.
A common method to overcome this problem is to use scripting. Manually designed rules are created to determine the course of action, for instance if the AI should attack or defend. While these methods are effective it is generally not enough to provide challenging play, especially to experienced players. Players can 'outsmart' a scripted AI simply by exploiting the predictability inherent to scripting.
An ideal AI is one which exhibits human-like behavoir. One which can adapt its strategy to cover holes exploited by players. One which has the ability of surprise, to make the player feel as if he is playing a thinking, cunning opponent instead of just 'trying to beat the computer'. Such an AI is not a pipe dream and even more to the point, is quite feasible from a technical standpoint.
This is a proposal to implement such an AI. I propose to use dynamic scripting to combine manually designed rulebases and online learning to create a customizable, extensible and adaptive AI. his type of dynamic planning has already proven successful in creating adaptive, formidable A.I. in games such as F.E.A.R. and Neverwinter Nights [1,2].
The rulebases will be implemented in FormulaAI while the reinforcement learning portion will be handled by C++. This accomplishes two goals: 1, the AI can easily be customized and extended without touching the C++ code and with no knowledge of the reinforcement learning process and 2, the learning process can easily be 'switched off', perhaps as a difficulty setting or to force strict adherence to a script.
Rulebases - Formula AI
Rulebases contain the FormulaAI rules the AI can use to form scripts. The rulebases developed for this project will form a repository of strategies and behaviors that designers can simply plug into a WML file to create a highly effective AI without ever touching FormulaAI or the underlying learning processes. Of course, if a designer desires a new strategy they can write custom FormulaAI rules.
I plan to involve the community in this portion of the project to identify the common strategies used by players and desired by scenario and MP bot designers. The how to play series will also serve as a useful guide. I foresee three categories of rulebases the designer can choose from to customize the AI for a particular scenario or deathmatch.
These rulebases will cover strategies to recruit units. A default recruitment strategy will be provided that selects the best units for the desired scenario goal.
Team rulebases will govern team strategies and will supply the majority of rules the AI can use when creating a script. Developed rulebases cover the majority of scenario and MP objectives, such as 'Escort unit x to hex y', 'Assassinate enemy leader', etc.
Unit rulebases provide rules for unit specific behavior. Some of these rulebases will be associated to units by default, for instance a 'healing' rule for healing units, 'backstab' to thieves, etc.
A variety of rulebases to customize unit behavior to a scenario storyline will also be available, such as 'Hatred towards faction x' and can be applied to single units or groups.
For example, a scenario designer might want to create a scenario in which a group of orcs, goblins and ogres must escort an orcish leader across a map to hex 5,10. The storyline might dictate that the goblins and ogres are only helping the orcs for a chance to kill elves, which the player has the ability to recruit. The scenario designer could implement this quite easily in their cfg file for that scenario with something like
[ai] [team_formula] rulebase = "escort" parameters = "Orcish Leader", 5, 10 [\team_formula] [unit_formula] apply_to_units = "goblins", "ogres" rulebase = "faction_hatred" parameters = "Elves" [\unit_faction] [\ai]
Of course the designer could make things a bit more complicated, by creating multiple team strategies associated to different units, e.g. suppose in the above example the AI also had a renegade faction of elvish rangers along for the ride, hellbent on destroying the human leader and don't really care about escorting the orcish leader. The designer might create an entirely new side, but if he or she wanted all the units on a single side, he might add the following to the above ai section.
[unit_formula] apply_to_units = "Elvish Ranger", "Elvish Avenger" rulebase = "hide_and_ambush" # Make elves stay hidden if possible until they attack [team_formula] # This will override the "escort" team formula rulebase = "assassinate" parameters = "Human Leader" [\team_formula] [\unit_formula]
The AI designer can go deeper or shallower if necessary. An adequate default AI with appropriate unit rulebases and a 'kill all' team rulebase will be the default if none are specified. If the provided rulebases do not cover some specific behavior, the designer of course may implement his own rulebase by creating a custom formula script or by altering the evaluation functions of the existing rulebases.
Adaptation and Learning - C++
The C++ portion of the project allows the AI to learn and adapt. Once the candidate moves are determined, the AI will rank the moves based on evaluation functions. The actual evaluation functions will be written in Formula AI, the C++ code should never have to be touch for customization or extension purposes.
There will be two evaluation functions: A team evaluation function, in which the impact of each move on the overall team strategy is evaluated and a unit evaluation function, in which the impact of a move on unit involved is evaluated. The results these functions determine the final evaluation of a move. Once all moves are evaluated, the script is formed based on these evaluations (i.e. best moves first).
At the end of turn, the success of each move is used to adjust a weight associated with that move. The weights are incorporated into the evaluation of moves during the next turn (e.g. weight * (unit_eval(formula) + team_eval(formula))). In this way, the AI can learn from it's mistakes and exploit holes in an opponent's strategy found by successful moves.
Online Adaptation of Game Opponent AI in Simulation and in Practice, Spronk et al.