Testing real-time artificial intelligence: an experience with Starcraft c
|
|
- Nathan Heath
- 6 years ago
- Views:
Transcription
1 Testing real-time artificial intelligence: an experience with Starcraft c game Cristian Conde, Mariano Moreno, and Diego C. Martínez Laboratorio de Investigación y Desarrollo en Inteligencia Artificial Universidad Nacional del Sur, Bahia Blanca, Argentina mail@cristianconde.com.ar, marianomoreno3@hotmail.com, dcm@cs.uns.edu.ar, WWW home page: Abstract. In this paper we describe the structure and behavior of an artificial intelligence bot capable of playing Starcraft. It was developed to participate in the AIIDE 2010 Starcraft Competition. Keywords: artificial intelligence, real time strategy games 1 Introduction Games are a subject of interest in Artificial Intelligence almost since the beginning of AI as a computer science discipline. Even more, complex game-related problems were solved by AI techniques before the popularization of videogames as an entertainment product [5]. Nowadays, a game genre that is very interesting in the AI field is simulation, and in particular real-time strategy (RTS) games. These games are more complex than traditional games, with several events occurring simultaneously within a dynamic environment. Basically, RTS games are simplified military simulations where the player must defeat the adversary by commanding virtual troops into a battlefield in real time. The production and improvement of troops is sustained by a simple self-contained economic model, usually based on the gathering of resources in the terrain, that can be exchanged by military units. In this game genre, player s reaction is supposed to be fast, and hence there is a limited time for reasoning [2]. There are several AI research areas that are very relevant to RTS games, such as reactive behavior, planning, collaboration and teamwork modeling, spatial and temporal reasoning, knowledge acquisition and learning, just to mention a few [4]. We are interested in artificial intelligence techniques suitable to be applied to real-time strategy games. Our line of research is mainly focused in the study of formalisms for knowledge representation and reasoning in order to achieve strategic thinking and planning in dynamic domains. 2 Real Time Strategy Testbeds As remarked in [2], most commercial RTS games lack of AI interfaces and then these systems cannot be used to conduct real-time AI research. Fortunately, there
2 are a few exceptions. In 2002, the University of Alberta launched a programming project called Open Real Time Strategy, or simply ORTS [1]. It is a programming environment for studying real-time AI problems such as pathfinding, dealing with imperfect information, scheduling and planning in the domain of RTS games. ORTS is a full game engine (licensed under GNU Public License), with a clientserver architecture where the server simulates the world and provides information to its clients. There is no predefined low-level behavior, so the client is responsible for every decision in the game, including basic pathfinding. In 2006, the first ORTS competition was held prior to Artificial Intelligence and Interactive Digital Entertainment - AIIDE 2006, with subsequent editions in 2007, and In 2010, Expressive Intelligence Studio at University of California (Santa Cruz), organized a real-time strategy AI competition using the commercial game Starcraft. The competition was hosted by AIIDE Bots for Starcraft can be developed using the Broodwar API, which provides hooks into the game. A C++ interface enable developers to query the current state of the world and issue orders to units. The competition consisted of four tournaments with different AI challenges. Basically the difference is about troops and terrain restrictions. The most challenging category is Tournament 4, a fully functional Starcraft match with fog of war. The bot must decide what to do in almost every aspect of the game: building locations, balance of troops according to their properties, technology research, group formations and general strategy. As expected, these decisions are supposed to be intelligent enough to seek the final victory. Our bot was built to play Tournament 4. It is a modest intelligent agent, focused in low-level artificial intelligence, setting the base for further research, as described in the following sections. 3 Our Starcraft bot The intelligent bot is developed using the BroodWar Application Programming Interface (BWAPI) 2, an open source C++ framework for creating AI modules for Starcraft Broodwar. Our particular bot for AIIDE 2010 was called Manolobot 3. BWAPI is used to sense the environment and to issue orders to individual units. It only reveals to AI modules the information that is normally visible to human players, which is considered a fair position to avoid cheating. Manolobot is formed by two main modules, Unit Manager and Strategy Manager. These modules are able to react to specific events on the game, for instance: onunitshow: triggered when a unit is visible on the map. onunitcreate: triggered when a new unit is created. onnukedetect: triggered when a nuclear launch was detected. These events (probably) require a process of decision making about the ongoing game. Hence, specific functions are invoked in response to these events. Modules are explained in the following sections. 1 We participated in that edition with an intelligent bot called LIDIABot
3 3.1 Strategy Manager The Strategy Manager decides which are the next goals to pursuit, given the situation in the game. Goals are divided into several stages, according to the common way of playing. 1. Gathering: creation of workers to gather minerals at an acceptable rate. 2. Defense: enables the construction of basic military buildings. 3. Tech Upgrade: enables the construction of advanced buildings and the starting of research activities (siege mode in tanks, weapons upgrade, etc). 4. Draft: battle companies are defined, based on diversity of troops. 5. Conclusive: enables de construction of other buildings (as starports and science lab). The construction of an additional base is considered. Stages are sequential and the transition between stages is determined by certain gameplay parameters. However, the goals established in some stages may be still active in subsequent stages. For example, in every stage the gathering of resources continues as they are available. 3.2 Unit Manager The Unit Manager captures the required low-level intelligence. It decides the order in which goals must be completed and the selection and location of buildings, as explained below. Exploring the world. A special worker is selected to be assigned to exploration tasks. The Unit Manager issues order to this scout, who goes around the map searching for enemies and resources. The scout is an expendable unit, and it is expected to be killed at some point. Scouts are sent in a regular fashion. If vultures are available, one of them can be selected as a scout. Setting up the neighborhood. The location of buildings is a classic low-level problem of RTS games. It requires a terrain analysis to identify obstacles, such as mountains, mines and other buildings. The type of the new building must also be considered as it influences the general distribution. Some buildings will produce new units nearby and then enough space must be available. Other buildings are just required for city growing, as depots (houses or farms in other games). Bunkers and defense turrets are better situated in places where the enemy is likely to attack. Manolobot builds depots in the borders of the map, leaving free space in the center of the base area. For the rest of the buildings, Manolobot searches for available space using a spiral search algorithm starting from the central base. The terrain analysis is made using the Broodwar Terrain Analyzer (BWTA) Library 4. In Figure 1, the border of a region is marked with green lines. Red lines identify corridors, or hallways, in the terrain. This is later used to decide on bunker positions. 4
4 Fig. 1. Hallway areas identified in terrain analysis Logistic support. The Unit Manager organizes the gathering of resources, minerals and gas. It prioritizes minerals over gas, since the former is required more often than the latter. Workers are balanced according to this prioritization. Also, when a building or a combat unit requires repairs, the Unit Manager select workers for this task. Group formations. Group formation is one of the most important AI aspects in RTS games. The Unit Manager makes groups of troops, called here companies, and the group of bunkers. There are two kind of companies: Attack and Defense. A third company, the Airborne, was partially designed but not included in the final bot. Fig. 2. A line of defense
5 Group of Bunkers. The line of defense is formed by three bunkers, two missile turrets and three siege tanks. The location is selected according to some information provided by the BWTA Library. This library identifies chokepoints, a part of a region that is a possible connection to another region in the map. Hence, it is a potential enemy entry zone. The Unit Manager calculates the correct orientation of the line of bunkers and turrets. In Figure 2, a complete line of defense is shown. This line is facing to a hallway, as expected. Attack Company. It is initially formed by marines and medics, but as the game advances, tanks, science vessel and goliaths are added. Companies are commanded by one of its members, according to its inner composition. The commander choses a target and leads the way, and it is used as a pivot unit for group formation. The selection of the commander is made according to available troops nearby. It may be a tank or a marine and formations follows concentric arcs, with (from center to periphery) tanks, goliaths, marines and medics, if available. When no tanks or marines are nearby, the company goes back to a designated area close to the base. In Figure 3, an initial attack company is shown. Some decorations are added for debugging purposes: marines are marked with red rectangles, and the commander is marked with a white circle. Troops in the rear front are medics, ready to heal wounded marines. Companies are sent to attack buildings. There is a previously defined safe meeting-point, where the company may retreat in order to acquire reinforces. Manolobot sends new units to this point. Defensive Company. This unit is stationed in the base and it is formed by marines, medics and ghosts. Enemies are chosen as they enter inside the perimeter of the base. Fig. 3. An early attack company
6 4 Conclusions Manolobot is capable of winning games against the built-in Starcraft bot. We decided to apply a defensive, conservative strategy, and focus the work in an ordered process of growth. We know this strategy is not necessarily optimal in a regular competition, where opponents sometimes use excessive rush of troops, but we are most interested in intelligent behavior than in trophies. The development of a Starcraft bot is a very challenging task, since there are several AI problems to solve, of different nature. In contrast to ORTS platform, some low-level AI problems are already solved, such as pathfinding. This allows for deeper elaborations on the intelligence of the bot, by focusing in high-level strategy, which is desired in our line of research. Even then, there are different issues of varied complexity that must be addressed. For space reasons these issues cannot be technically described here, but some remarks can be done. 1. Micro-management requires specific AI techniques. We use an algorithm to manage workers and its goals (mineral and gas mining, building repairs). Troop formations uses an algorithm based on the proximity of troops and a pivot unit to define orientation and topology of formations. A lot of time is required to solve this problems before addressing macro-strategy. 2. High-level strategy must be adaptive. Although there is a general strategic plan, the intelligence of the opposing bot may vary and then some adjustments to the current strategy must be made. This is specially important in competitions, since several games are played with different bots. We choose to follow a general strategy as described before in the Strategy Manager, but some improvements must be made. As intended, most of the low-level AI problems were addressed and successfully solved in Manolobot. This was the first step in an extensive line of research. Our intention is to connect the Starcraft bot with a formalism of nonmonotonic reasoning for high-level planning. This allows a dynamic process of decision making. We are specially interested in DeLP-Defeasible Logic Programming [3]. A DeLP server is already available to infer decisions from a knowledge base. Unfortunately, the development of a bot is sometimes delayed by several bugs in the Broodwar API that leads to application crashes. References 1. Buro, M.: Orts: A hack-free rts game environment. In: Proceedings of the Third International Conference on Computers and Games. pp (2002) 2. Buro, M.: Real time strategy. new ai research. In: Proceedings of the International Joint Conference on AI pp Acapulco, Mexico (2003) 3. García, A.J., Simari, G.R.: Defeasible logic programming: An argumentative approach. Theory and Practice of Logic Programming 4(1-2), (2004) 4. John Laird, M.v.L.: Human-level ai s killer application: Interactive computer games. In: Proceedings of the 17th. AAAI National Conference on AI 2000 and 12th Ann. Conf. Innov. Appl. AI. pp Texas, US (2000) 5. Shannon, C.: Programming a computer playing chess. Philosophical Magazine 41(314) (1950)
Potential-Field Based navigation in StarCraft
Potential-Field Based navigation in StarCraft Johan Hagelbäck, Member, IEEE Abstract Real-Time Strategy (RTS) games are a sub-genre of strategy games typically taking place in a war setting. RTS games
More informationElectronic Research Archive of Blekinge Institute of Technology
Electronic Research Archive of Blekinge Institute of Technology http://www.bth.se/fou/ This is an author produced version of a conference paper. The paper has been peer-reviewed but may not include the
More informationA Multi-Agent Potential Field-Based Bot for a Full RTS Game Scenario
Proceedings of the Fifth Artificial Intelligence for Interactive Digital Entertainment Conference A Multi-Agent Potential Field-Based Bot for a Full RTS Game Scenario Johan Hagelbäck and Stefan J. Johansson
More informationApplying Goal-Driven Autonomy to StarCraft
Applying Goal-Driven Autonomy to StarCraft Ben G. Weber, Michael Mateas, and Arnav Jhala Expressive Intelligence Studio UC Santa Cruz bweber,michaelm,jhala@soe.ucsc.edu Abstract One of the main challenges
More informationReactive Planning for Micromanagement in RTS Games
Reactive Planning for Micromanagement in RTS Games Ben Weber University of California, Santa Cruz Department of Computer Science Santa Cruz, CA 95064 bweber@soe.ucsc.edu Abstract This paper presents an
More informationExtending the STRADA Framework to Design an AI for ORTS
Extending the STRADA Framework to Design an AI for ORTS Laurent Navarro and Vincent Corruble Laboratoire d Informatique de Paris 6 Université Pierre et Marie Curie (Paris 6) CNRS 4, Place Jussieu 75252
More informationCase-Based Goal Formulation
Case-Based Goal Formulation Ben G. Weber and Michael Mateas and Arnav Jhala Expressive Intelligence Studio University of California, Santa Cruz {bweber, michaelm, jhala}@soe.ucsc.edu Abstract Robust AI
More informationCase-Based Goal Formulation
Case-Based Goal Formulation Ben G. Weber and Michael Mateas and Arnav Jhala Expressive Intelligence Studio University of California, Santa Cruz {bweber, michaelm, jhala}@soe.ucsc.edu Abstract Robust AI
More informationThe Second Annual Real-Time Strategy Game AI Competition
The Second Annual Real-Time Strategy Game AI Competition Michael Buro, Marc Lanctot, and Sterling Orsten Department of Computing Science University of Alberta, Edmonton, Alberta, Canada {mburo lanctot
More informationReactive Strategy Choice in StarCraft by Means of Fuzzy Control
Mike Preuss Comp. Intelligence Group TU Dortmund mike.preuss@tu-dortmund.de Reactive Strategy Choice in StarCraft by Means of Fuzzy Control Daniel Kozakowski Piranha Bytes, Essen daniel.kozakowski@ tu-dortmund.de
More informationUSING A FUZZY LOGIC CONTROL SYSTEM FOR AN XPILOT COMBAT AGENT ANDREW HUBLEY AND GARY PARKER
World Automation Congress 21 TSI Press. USING A FUZZY LOGIC CONTROL SYSTEM FOR AN XPILOT COMBAT AGENT ANDREW HUBLEY AND GARY PARKER Department of Computer Science Connecticut College New London, CT {ahubley,
More informationA Particle Model for State Estimation in Real-Time Strategy Games
Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment A Particle Model for State Estimation in Real-Time Strategy Games Ben G. Weber Expressive Intelligence
More informationHigh-Level Representations for Game-Tree Search in RTS Games
Artificial Intelligence in Adversarial Real-Time Games: Papers from the AIIDE Workshop High-Level Representations for Game-Tree Search in RTS Games Alberto Uriarte and Santiago Ontañón Computer Science
More informationMulti-Agent Potential Field Based Architectures for
Multi-Agent Potential Field Based Architectures for Real-Time Strategy Game Bots Johan Hagelbäck Blekinge Institute of Technology Doctoral Dissertation Series No. 2012:02 School of Computing Multi-Agent
More informationSORTS: A Human-Level Approach to Real-Time Strategy AI
SORTS: A Human-Level Approach to Real-Time Strategy AI Sam Wintermute, Joseph Xu, and John E. Laird University of Michigan 2260 Hayward St. Ann Arbor, MI 48109-2121 {swinterm, jzxu, laird}@umich.edu Abstract
More informationUsing Automated Replay Annotation for Case-Based Planning in Games
Using Automated Replay Annotation for Case-Based Planning in Games Ben G. Weber 1 and Santiago Ontañón 2 1 Expressive Intelligence Studio University of California, Santa Cruz bweber@soe.ucsc.edu 2 IIIA,
More informationGame Artificial Intelligence ( CS 4731/7632 )
Game Artificial Intelligence ( CS 4731/7632 ) Instructor: Stephen Lee-Urban http://www.cc.gatech.edu/~surban6/2018-gameai/ (soon) Piazza T-square What s this all about? Industry standard approaches to
More informationEfficient Resource Management in StarCraft: Brood War
Efficient Resource Management in StarCraft: Brood War DAT5, Fall 2010 Group d517a 7th semester Department of Computer Science Aalborg University December 20th 2010 Student Report Title: Efficient Resource
More informationIntegrating Learning in a Multi-Scale Agent
Integrating Learning in a Multi-Scale Agent Ben Weber Dissertation Defense May 18, 2012 Introduction AI has a long history of using games to advance the state of the field [Shannon 1950] Real-Time Strategy
More informationCS 680: GAME AI WEEK 4: DECISION MAKING IN RTS GAMES
CS 680: GAME AI WEEK 4: DECISION MAKING IN RTS GAMES 2/6/2012 Santiago Ontañón santi@cs.drexel.edu https://www.cs.drexel.edu/~santi/teaching/2012/cs680/intro.html Reminders Projects: Project 1 is simpler
More informationA Multi-Agent Potential Field Based Approach for Real-Time Strategy Game Bots. Johan Hagelbäck
A Multi-Agent Potential Field Based Approach for Real-Time Strategy Game Bots Johan Hagelbäck c 2009 Johan Hagelbäck Department of Systems and Software Engineering School of Engineering Publisher: Blekinge
More informationA NEW SIMULATION FRAMEWORK OF OPERATIONAL EFFECTIVENESS ANALYSIS FOR UNMANNED GROUND VEHICLE
A NEW SIMULATION FRAMEWORK OF OPERATIONAL EFFECTIVENESS ANALYSIS FOR UNMANNED GROUND VEHICLE 1 LEE JAEYEONG, 2 SHIN SUNWOO, 3 KIM CHONGMAN 1 Senior Research Fellow, Myongji University, 116, Myongji-ro,
More informationArtificial Intelligence ( CS 365 ) IMPLEMENTATION OF AI SCRIPT GENERATOR USING DYNAMIC SCRIPTING FOR AOE2 GAME
Artificial Intelligence ( CS 365 ) IMPLEMENTATION OF AI SCRIPT GENERATOR USING DYNAMIC SCRIPTING FOR AOE2 GAME Author: Saurabh Chatterjee Guided by: Dr. Amitabha Mukherjee Abstract: I have implemented
More informationAsymmetric potential fields
Master s Thesis Computer Science Thesis no: MCS-2011-05 January 2011 Asymmetric potential fields Implementation of Asymmetric Potential Fields in Real Time Strategy Game Muhammad Sajjad Muhammad Mansur-ul-Islam
More informationAI System Designs for the First RTS-Game AI Competition
AI System Designs for the First RTS-Game AI Competition Michael Buro, James Bergsma, David Deutscher, Timothy Furtak, Frantisek Sailer, David Tom, Nick Wiebe Department of Computing Science University
More informationOperation Blue Metal Event Outline. Participant Requirements. Patronage Card
Operation Blue Metal Event Outline Operation Blue Metal is a Strategic event that allows players to create a story across connected games over the course of the event. Follow the instructions below in
More informationBuilding Placement Optimization in Real-Time Strategy Games
Building Placement Optimization in Real-Time Strategy Games Nicolas A. Barriga, Marius Stanescu, and Michael Buro Department of Computing Science University of Alberta Edmonton, Alberta, Canada, T6G 2E8
More informationAdjutant Bot: An Evaluation of Unit Micromanagement Tactics
Adjutant Bot: An Evaluation of Unit Micromanagement Tactics Nicholas Bowen Department of EECS University of Central Florida Orlando, Florida USA Email: nicholas.bowen@knights.ucf.edu Jonathan Todd Department
More informationSTRATEGO EXPERT SYSTEM SHELL
STRATEGO EXPERT SYSTEM SHELL Casper Treijtel and Leon Rothkrantz Faculty of Information Technology and Systems Delft University of Technology Mekelweg 4 2628 CD Delft University of Technology E-mail: L.J.M.Rothkrantz@cs.tudelft.nl
More informationChapter 14 Optimization of AI Tactic in Action-RPG Game
Chapter 14 Optimization of AI Tactic in Action-RPG Game Kristo Radion Purba Abstract In an Action RPG game, usually there is one or more player character. Also, there are many enemies and bosses. Player
More informationEvaluating a Cognitive Agent-Orientated Approach for the creation of Artificial Intelligence. Tom Peeters
Evaluating a Cognitive Agent-Orientated Approach for the creation of Artificial Intelligence in StarCraft Tom Peeters Evaluating a Cognitive Agent-Orientated Approach for the creation of Artificial Intelligence
More informationArtificial Intelligence Paper Presentation
Artificial Intelligence Paper Presentation Human-Level AI s Killer Application Interactive Computer Games By John E.Lairdand Michael van Lent ( 2001 ) Fion Ching Fung Li ( 2010-81329) Content Introduction
More informationGame-Tree Search over High-Level Game States in RTS Games
Proceedings of the Tenth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2014) Game-Tree Search over High-Level Game States in RTS Games Alberto Uriarte and
More informationBayesian Networks for Micromanagement Decision Imitation in the RTS Game Starcraft
Bayesian Networks for Micromanagement Decision Imitation in the RTS Game Starcraft Ricardo Parra and Leonardo Garrido Tecnológico de Monterrey, Campus Monterrey Ave. Eugenio Garza Sada 2501. Monterrey,
More informationImplementing a Wall-In Building Placement in StarCraft with Declarative Programming
Implementing a Wall-In Building Placement in StarCraft with Declarative Programming arxiv:1306.4460v1 [cs.ai] 19 Jun 2013 Michal Čertický Agent Technology Center, Czech Technical University in Prague michal.certicky@agents.fel.cvut.cz
More informationFederico Forti, Erdi Izgi, Varalika Rathore, Francesco Forti
Basic Information Project Name Supervisor Kung-fu Plants Jakub Gemrot Annotation Kung-fu plants is a game where you can create your characters, train them and fight against the other chemical plants which
More information2 The Engagement Decision
1 Combat Outcome Prediction for RTS Games Marius Stanescu, Nicolas A. Barriga and Michael Buro [1 leave this spacer to make page count accurate] [2 leave this spacer to make page count accurate] [3 leave
More informationA Benchmark for StarCraft Intelligent Agents
Artificial Intelligence in Adversarial Real-Time Games: Papers from the AIIDE 2015 Workshop A Benchmark for StarCraft Intelligent Agents Alberto Uriarte and Santiago Ontañón Computer Science Department
More informationAI in Computer Games. AI in Computer Games. Goals. Game A(I?) History Game categories
AI in Computer Games why, where and how AI in Computer Games Goals Game categories History Common issues and methods Issues in various game categories Goals Games are entertainment! Important that things
More informationResearch Article A Multiagent Potential Field-Based Bot for Real-Time Strategy Games
Computer Games Technology Volume 2009, Article ID 910819, 10 pages doi:10.1155/2009/910819 Research Article A Multiagent Potential Field-Based Bot for Real-Time Strategy Games Johan Hagelbäck and Stefan
More informationStrategic and Tactical Reasoning with Waypoints Lars Lidén Valve Software
Strategic and Tactical Reasoning with Waypoints Lars Lidén Valve Software lars@valvesoftware.com For the behavior of computer controlled characters to become more sophisticated, efficient algorithms are
More informationIMPROVING TOWER DEFENSE GAME AI (DIFFERENTIAL EVOLUTION VS EVOLUTIONARY PROGRAMMING) CHEAH KEEI YUAN
IMPROVING TOWER DEFENSE GAME AI (DIFFERENTIAL EVOLUTION VS EVOLUTIONARY PROGRAMMING) CHEAH KEEI YUAN FACULTY OF COMPUTING AND INFORMATICS UNIVERSITY MALAYSIA SABAH 2014 ABSTRACT The use of Artificial Intelligence
More informationEvolving Effective Micro Behaviors in RTS Game
Evolving Effective Micro Behaviors in RTS Game Siming Liu, Sushil J. Louis, and Christopher Ballinger Evolutionary Computing Systems Lab (ECSL) Dept. of Computer Science and Engineering University of Nevada,
More informationOpponent Modelling In World Of Warcraft
Opponent Modelling In World Of Warcraft A.J.J. Valkenberg 19th June 2007 Abstract In tactical commercial games, knowledge of an opponent s location is advantageous when designing a tactic. This paper proposes
More informationProject Number: SCH-1102
Project Number: SCH-1102 LEARNING FROM DEMONSTRATION IN A GAME ENVIRONMENT A Major Qualifying Project Report submitted to the Faculty of WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements
More informationAGENT PLATFORM FOR ROBOT CONTROL IN REAL-TIME DYNAMIC ENVIRONMENTS. Nuno Sousa Eugénio Oliveira
AGENT PLATFORM FOR ROBOT CONTROL IN REAL-TIME DYNAMIC ENVIRONMENTS Nuno Sousa Eugénio Oliveira Faculdade de Egenharia da Universidade do Porto, Portugal Abstract: This paper describes a platform that enables
More informationOperation Deep Jungle Event Outline. Participant Requirements. Patronage Card
Operation Deep Jungle Event Outline Operation Deep Jungle is a Raid event that concentrates on a player s units and how they grow through upgrades, abilities, and even fatigue over the course of the event.
More informationBasic Tips & Tricks To Becoming A Pro
STARCRAFT 2 Basic Tips & Tricks To Becoming A Pro 1 P age Table of Contents Introduction 3 Choosing Your Race (for Newbies) 3 The Economy 4 Tips & Tricks 6 General Tips 7 Battle Tips 8 How to Improve Your
More informationStarcraft Invasions a solitaire game. By Eric Pietrocupo January 28th, 2012 Version 1.2
Starcraft Invasions a solitaire game By Eric Pietrocupo January 28th, 2012 Version 1.2 Introduction The Starcraft board game is very complex and long to play which makes it very hard to find players willing
More informationMFF UK Prague
MFF UK Prague 25.10.2018 Source: https://wall.alphacoders.com/big.php?i=324425 Adapted from: https://wall.alphacoders.com/big.php?i=324425 1996, Deep Blue, IBM AlphaGo, Google, 2015 Source: istan HONDA/AFP/GETTY
More informationThe X Rebirth 3.0 TLDR manual
The X Rebirth 3.0 TLDR manual An overview of new features in version 3.0 of X Rebirth Faster playing Abort cutscenes: All cutscenes can now be aborted by pressing escape (e.g getting out of the Albion
More informationIMGD 1001: Programming Practices; Artificial Intelligence
IMGD 1001: Programming Practices; Artificial Intelligence Robert W. Lindeman Associate Professor Department of Computer Science Worcester Polytechnic Institute gogo@wpi.edu Outline Common Practices Artificial
More informationCenter for Cognitive Architectures University of Michigan 2260 Hayward Ave Ann Arbor, Michigan TECHNICAL REPORT CCA-TR SORTS:
Center for Cognitive Architectures University of Michigan 2260 Hayward Ave Ann Arbor, Michigan 48109-2121 TECHNICAL REPORT CCA-TR-2007-01 SORTS: INTEGRATING SOAR WITH A REAL-TIME STRATEGY GAME Investigators
More informationMonte Carlo Planning in RTS Games
Abstract- Monte Carlo simulations have been successfully used in classic turn based games such as backgammon, bridge, poker, and Scrabble. In this paper, we apply the ideas to the problem of planning in
More informationApproximation Models of Combat in StarCraft 2
Approximation Models of Combat in StarCraft 2 Ian Helmke, Daniel Kreymer, and Karl Wiegand Northeastern University Boston, MA 02115 {ihelmke, dkreymer, wiegandkarl} @gmail.com December 3, 2012 Abstract
More informationCombining Scripted Behavior with Game Tree Search for Stronger, More Robust Game AI
1 Combining Scripted Behavior with Game Tree Search for Stronger, More Robust Game AI Nicolas A. Barriga, Marius Stanescu, and Michael Buro [1 leave this spacer to make page count accurate] [2 leave this
More informationA CBR/RL system for learning micromanagement in real-time strategy games
A CBR/RL system for learning micromanagement in real-time strategy games Martin Johansen Gunnerud Master of Science in Computer Science Submission date: June 2009 Supervisor: Agnar Aamodt, IDI Norwegian
More informationCapturing and Adapting Traces for Character Control in Computer Role Playing Games
Capturing and Adapting Traces for Character Control in Computer Role Playing Games Jonathan Rubin and Ashwin Ram Palo Alto Research Center 3333 Coyote Hill Road, Palo Alto, CA 94304 USA Jonathan.Rubin@parc.com,
More informationIMGD 1001: Programming Practices; Artificial Intelligence
IMGD 1001: Programming Practices; Artificial Intelligence by Mark Claypool (claypool@cs.wpi.edu) Robert W. Lindeman (gogo@wpi.edu) Outline Common Practices Artificial Intelligence Claypool and Lindeman,
More informationNOVA. Game Pitch SUMMARY GAMEPLAY LOOK & FEEL. Story Abstract. Appearance. Alex Tripp CIS 587 Fall 2014
Alex Tripp CIS 587 Fall 2014 NOVA Game Pitch SUMMARY Story Abstract Aliens are attacking the Earth, and it is up to the player to defend the planet. Unfortunately, due to bureaucratic incompetence, only
More informationAdjustable Group Behavior of Agents in Action-based Games
Adjustable Group Behavior of Agents in Action-d Games Westphal, Keith and Mclaughlan, Brian Kwestp2@uafortsmith.edu, brian.mclaughlan@uafs.edu Department of Computer and Information Sciences University
More informationWho am I? AI in Computer Games. Goals. AI in Computer Games. History Game A(I?)
Who am I? AI in Computer Games why, where and how Lecturer at Uppsala University, Dept. of information technology AI, machine learning and natural computation Gamer since 1980 Olle Gällmo AI in Computer
More informationGoal-Directed Hierarchical Dynamic Scripting for RTS Games
Goal-Directed Hierarchical Dynamic Scripting for RTS Games Anders Dahlbom & Lars Niklasson School of Humanities and Informatics University of Skövde, Box 408, SE-541 28 Skövde, Sweden anders.dahlbom@his.se
More informationBasic AI Techniques for o N P N C P C Be B h e a h v a i v ou o r u s: s FS F T S N
Basic AI Techniques for NPC Behaviours: FSTN Finite-State Transition Networks A 1 a 3 2 B d 3 b D Action State 1 C Percept Transition Team Buddies (SCEE) Introduction Behaviours characterise the possible
More informationVolume 4, Number 2 Government and Defense September 2011
Volume 4, Number 2 Government and Defense September 2011 Editor-in-Chief Managing Editor Guest Editors Jeremiah Spence Yesha Sivan Paulette Robinson, National Defense University, USA Michael Pillar, National
More informationAn Improved Dataset and Extraction Process for Starcraft AI
Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference An Improved Dataset and Extraction Process for Starcraft AI Glen Robertson and Ian Watson Department
More informationBOLT ACTION COMBAT PATROL
THURSDAY :: MARCH 23 6:00 PM 11:45 PM BOLT ACTION COMBAT PATROL Do not lose this packet! It contains all necessary missions and results sheets required for you to participate in today s tournament. It
More informationDOMINATION PLAYER A PLAYER B
DOMINATION The battlefield will provide a distinct tactical advantage for whoever holds it for many years to come. Victory can be achieved by forcing the enemy back and securing the key points on the battlefield,
More informationBATTLEFIELD TERRAIN STC RYZA-PATTERN RUINS
BATTLEFIELD TERRAIN In this section you will find expanded terrain rules for the STC Ryza-pattern Ruins included in Moon Base Klaisus. You do not need to use these rules to enjoy a battle using the models,
More informationRTS Games and Real Time AI Research
RTS Games and Real Time AI Research Michael Buro & Timothy M. Furtak Department of Computing Science, University of Alberta, Edmonton, AB, T6J 2E8, Canada email: (mburo furtak)@cs.ualberta.ca Abstract
More informationOn the day you also need to bring :
In this pack you will find everything you will need to do and know, to prepare for and play in the OMG Bolt Action Tournament. Tournament Organiser: Jeff Black Players Pack/ Tournament Rules writer: Jeff
More informationOil Rush user manual. Hardware Requirements. Minimal. Recommended
Oil Rush user manual Oil Rush is a real-time strategy game based on group control. It offers mechanics of a classical RTS combined with a Tower Defense genre: control the upgrade of production platforms
More informationChapter 2 Threat FM 20-3
Chapter 2 Threat The enemy uses a variety of sensors to detect and identify US soldiers, equipment, and supporting installations. These sensors use visual, ultraviolet (W), infared (IR), radar, acoustic,
More informationCS 480: GAME AI DECISION MAKING AND SCRIPTING
CS 480: GAME AI DECISION MAKING AND SCRIPTING 4/24/2012 Santiago Ontañón santi@cs.drexel.edu https://www.cs.drexel.edu/~santi/teaching/2012/cs480/intro.html Reminders Check BBVista site for the course
More informationLearning Unit Values in Wargus Using Temporal Differences
Learning Unit Values in Wargus Using Temporal Differences P.J.M. Kerbusch 16th June 2005 Abstract In order to use a learning method in a computer game to improve the perfomance of computer controlled entities,
More informationUCT for Tactical Assault Planning in Real-Time Strategy Games
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09) UCT for Tactical Assault Planning in Real-Time Strategy Games Radha-Krishna Balla and Alan Fern School
More informationCS 387/680: GAME AI DECISION MAKING. 4/19/2016 Instructor: Santiago Ontañón
CS 387/680: GAME AI DECISION MAKING 4/19/2016 Instructor: Santiago Ontañón santi@cs.drexel.edu Class website: https://www.cs.drexel.edu/~santi/teaching/2016/cs387/intro.html Reminders Check BBVista site
More informationElements of Artificial Intelligence and Expert Systems
Elements of Artificial Intelligence and Expert Systems Master in Data Science for Economics, Business & Finance Nicola Basilico Dipartimento di Informatica Via Comelico 39/41-20135 Milano (MI) Ufficio
More informationChapter 5: Game Analytics
Lecture Notes for Managing and Mining Multiplayer Online Games Summer Semester 2017 Chapter 5: Game Analytics Lecture Notes 2012 Matthias Schubert http://www.dbs.ifi.lmu.de/cms/vo_managing_massive_multiplayer_online_games
More informationIndividual Test Item Specifications
Individual Test Item Specifications 8208110 Game and Simulation Foundations 2015 The contents of this document were developed under a grant from the United States Department of Education. However, the
More informationCombining Expert Knowledge and Learning from Demonstration in Real-Time Strategy Games
Combining Expert Knowledge and Learning from Demonstration in Real-Time Strategy Games Ricardo Palma, Antonio A. Sánchez-Ruiz, Marco A. Gómez-Martín, Pedro P. Gómez-Martín and Pedro A. González-Calero
More informationFleet Engagement. Mission Objective. Winning. Mission Special Rules. Set Up. Game Length
Fleet Engagement Mission Objective Your forces have found the enemy and they are yours! Man battle stations, clear for action!!! Mission Special Rules None Set Up velocity up to three times their thrust
More informationCharles University in Prague. Faculty of Mathematics and Physics BACHELOR THESIS. Pavel Šmejkal
Charles University in Prague Faculty of Mathematics and Physics BACHELOR THESIS Pavel Šmejkal Integrating Probabilistic Model for Detecting Opponent Strategies Into a Starcraft Bot Department of Software
More informationUser Research in Fractal Spaces:
User Research in Fractal Spaces: Behavioral analytics: Profiling users and informing game design Collaboration with national and international researchers & companies Behavior prediction and monetization:
More informationSCAIL: An integrated Starcraft AI System
SCAIL: An integrated Starcraft AI System Jay Young, Fran Smith, Christopher Atkinson, Ken Poyner and Tom Chothia Abstract We present the work on our integrated AI system SCAIL, which is capable of playing
More informationthe question of whether computers can think is like the question of whether submarines can swim -- Dijkstra
the question of whether computers can think is like the question of whether submarines can swim -- Dijkstra Game AI: The set of algorithms, representations, tools, and tricks that support the creation
More informationGetting Started with Panzer Campaigns: Budapest 45
Getting Started with Panzer Campaigns: Budapest 45 Welcome to Panzer Campaigns Budapest 45. In this, the seventeenth title in of the Panzer Campaigns series of operational combat in World War II, we are
More informationComprehensive Rules Document v1.1
Comprehensive Rules Document v1.1 Contents 1. Game Concepts 100. General 101. The Golden Rule 102. Players 103. Starting the Game 104. Ending The Game 105. Kairu 106. Cards 107. Characters 108. Abilities
More informationRange Example. CARDS Most Wanted The special rule for the Most Wanted objective card should read:
Range Example FAQ Version 1.1.1 / Updated 7.24.2015 This document contains frequently asked questions, rule clarifications, and errata for Star Wars: Armada. All changes and additions made to this document
More informationCS325 Artificial Intelligence Ch. 5, Games!
CS325 Artificial Intelligence Ch. 5, Games! Cengiz Günay, Emory Univ. vs. Spring 2013 Günay Ch. 5, Games! Spring 2013 1 / 19 AI in Games A lot of work is done on it. Why? Günay Ch. 5, Games! Spring 2013
More informationOfficial Rules Clarification, Frequently Asked Questions, and Errata
Official Rules Clarification, Frequently Asked Questions, and Errata 02/22/2013 - Version 1.1 New Content: Framework Effect (page 3), Card Effect (page 3) 1 This section contains the official clarifications
More informationA Bayesian Model for Plan Recognition in RTS Games applied to StarCraft
1/38 A Bayesian for Plan Recognition in RTS Games applied to StarCraft Gabriel Synnaeve and Pierre Bessière LPPA @ Collège de France (Paris) University of Grenoble E-Motion team @ INRIA (Grenoble) October
More informationPrinciples of Computer Game Design and Implementation. Lecture 20
Principles of Computer Game Design and Implementation Lecture 20 utline for today Sense-Think-Act Cycle: Thinking Acting 2 Agents and Virtual Player Agents, no virtual player Shooters, racing, Virtual
More informationPredicting Army Combat Outcomes in StarCraft
Proceedings of the Ninth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Predicting Army Combat Outcomes in StarCraft Marius Stanescu, Sergio Poo Hernandez, Graham Erickson,
More informationUnit List Hot Spot Fixed
Getting Started This file contains instructions on how to get started with the Fulda Gap 85 software. If it is not already running, you should run the Main Program by clicking on the Main Program entry
More informationRange Example. Cards Most Wanted The special rule for the Most Wanted objective card should read:
Range Example FAQ Version 1.2 / Updated 9.30.2015 This document contains frequently asked questions, rule clarifications, and errata for Star Wars: Armada. All changes and additions made to this document
More informationPotential Flows for Controlling Scout Units in StarCraft
Potential Flows for Controlling Scout Units in StarCraft Kien Quang Nguyen, Zhe Wang, and Ruck Thawonmas Intelligent Computer Entertainment Laboratory, Graduate School of Information Science and Engineering,
More informationTexas Hold em Inference Bot Proposal. By: Brian Mihok & Michael Terry Date Due: Monday, April 11, 2005
Texas Hold em Inference Bot Proposal By: Brian Mihok & Michael Terry Date Due: Monday, April 11, 2005 1 Introduction One of the key goals in Artificial Intelligence is to create cognitive systems that
More informationOFFensive Swarm-Enabled Tactics (OFFSET)
OFFensive Swarm-Enabled Tactics (OFFSET) Dr. Timothy H. Chung, Program Manager Tactical Technology Office Briefing Prepared for OFFSET Proposers Day 1 Why are Swarms Hard: Complexity of Swarms Number Agent
More informationSequential Pattern Mining in StarCraft:Brood War for Short and Long-term Goals
Sequential Pattern Mining in StarCraft:Brood War for Short and Long-term Goals Anonymous Submitted for blind review Workshop on Artificial Intelligence in Adversarial Real-Time Games AIIDE 2014 Abstract
More information