Query: Team bot ai

title: Dota 2 Bot Competition
abstract: Multiplayer online battle arena (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long term planning, within a real time combined action and strategy gameplay. Artificial intelligence (AI) and computational intelligence (CI) in games research competitions offer a wide variety of challenges regarding the study and application of AI techniques to different game genres. These events are widely accepted by the AI/CI community as a sort of AI benchmarking that strongly influences many other research areas in the field. This paper presents and describes in detail the Dota 2 (Defense of the Ancients 2) Bot competition and the Dota 2 AI framework that supports it. This challenge aims to join both the MOBAs and AI/CI game competitions, inviting participants to submit AI controllers for the successful MOBA Dota 2 to play in 1v1 matches, which aims for fostering research on AI techniques for real time games. The Dota 2 AI framework makes use of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original F2P game.

title: Evolving Bots AI in Unreal TM
abstract: A bot is an autonomous enemy which tries to beat the human player and/or some other bots in a game. This chapter describes the design, implementation and results of a system to evolve bots inside the PC game UnrealTM. The default artificial intelligence (AI) of this bot has been improved using two different evolutionary methods: genetic algorithms (GAs) and genetic programming (GP) The first one has been applied for tuning the parameters of the hard coded values inside the bot AI code. The second method has been used to change the default set of rules (or states) that defines its behaviour. Moreover, the first approach has been considered at two levels: individual and team, performing different studies at the latter level, looking for the best cooperation scheme. Both techniques yield very good results, evolving bots (and teams) which are capable of defeating the default ones. The best results are obtained for the GA approach, since it just performs a refinement considering the default behaviour rules, while the GP method has to redefine the whole set of rules, so it is harder to get good results. This chapter presents one possibility of AI programming: building a better model from a standard one.

title: PREDIKSI KEMENANGAN BOT DOTA 2 MENGGUNAKAN METODE NAIVE BAYES
abstract: Dota 2 adalah salah satu game multiplayer yang banyak dimainkan saat ini. Dalam memainkan game dota 2, pemain dibagi menjadi dua kelompok yang saling bertempur, terdiri dari Radiant dan Dire. Masing masing kelompok terdiri dari lima pemain. Didalam game dota 2 terdapat jenis permainan custom scenario dimana hero musuh dijalankan oleh artificial intelligences (AI) Ketika memainkan versi scenario, pemain bisa dibantu oleh hero dota sekutu yang dijalankan oleh AI game. Masing masing team memiliki markas utama yang harus dilindungi, markas ini disebut Ancient. Ketika ancient salah satu team hancur, maka pertandingan selesai. Naive bayes adalah algoritma data mining klasifikasi yang digunakan untuk prediksi kemenangan bot dalam pertandingan dota 2. Dengan pembobotan statistik pada masing masing bot akan dilakukan prediksi kemenangan bot pada game dota 2 sehingga akan diketahui team radiant atau dire yang akan memenangkan pertandingan dalam game dota 2. Akurasi yang dihasilkan naive bayes untuk memprediksi kemenangan team dota 2 adalah sebesar 54.17%

title: A Pilot Study Integrating an AI driven Chatbot in an Introductory Programming Course
abstract: As AI software tools become more commonplace, their potential to transform the student experience has greatly increased. By integrating these tools into a course, students can begin to receive real time, around the clock, instructor moderated support. The potential benefits for students are significant. In addition to increasing their ability to work constructively on their schedule, it has the potential to engage students who might otherwise avoid directly interacting with another person. Before these tools can be meaningfully actualized into a course, work must be done to build a knowledge base about the course. For this research, the team sought to develop an intelligent chatbot interface for an introductory computer programming course. The interface had an initially limited knowledge base with the intent that it would be populated based on students' interactions with the chatbot. This model allowed the bot to evolve with the needs of the students. This paper seeks to present the methodology for how the chatbot was developed and integrated into the course, how the knowledge base was developed, the usage during the pilot, and the next steps for improving the chatbot's interface. Additionally, the paper will discuss the mechanisms added to handle issues such as false positive responses and how faculty may be able to integrate such tools into their own courses as supplementary assistance.

title: Prolog Scripted Tactics Negotiation and Coordinated Team Actions for Counter Strike Game Bots
abstract: k labs is a research project exploring the possibilities of the logic programming paradigm in bot behavior programming for first person shooter (FPS) games. The focus of previous work was to make Prolog a usable tool for bot programming and a baseline for further extensions. This paper presents one such extension, which makes it possible to script tactics of the entire team of bots. The algorithm was tested by bot to bot computer tests and by running surveys among human players who volunteered to take part in the research. The results of the both tests are presented in this paper. The extension itself demonstrates the flexibility of the framework. Although the proposed method for defining team behaviors relies solely on the knowledge of the bot's designer, alternative approaches, which use rules that are obtained by computational techniques, can also be developed. Such approaches are also being investigated as part of the k labs project.

title: The awakening of conscious bots Conscious Robots team winner of the 2 K BotPrize 2010 contest
abstract: Most of current efforts in the development of believable bots bots that behave like human players are based on classical AI techniques. These techniques are based on relatively old principles, which nevertheless are being progressively improved or wisely adapted increasing their performance in order to satisfy new game requirements. Taking a different perspective, the approach that we adopted for the design of our bot (CC Bot2) was rather opposed to this trend. Specifically, we implemented a computational model of the Global Workspace Theory (Baars, 1988) a kind of shared memory space where different agents that we call specialized processors can collaborate and compete with each other dynamically  We believe that applying new techniques from the field of Machine Consciousness might also provide good results, even in the short term.

title: DotA 2 bots win prediction using naive bayes based on adaboost algorithm
abstract: DotA 2 is a multiplayer game that is widely played today. In DotA 2, the players are divided into two teams, i.e. radiant and dire to against each other. Each team consists of five heroes. One hero is played by human and four heroes are controlled by artificial intelligence (AI) In our prediction, we only collect the statistical data of AI heroes. Each team has the main headquarters, which needs to be protected; the headquarters is a called ancient. When the ancient of a team is destroyed, then the game is over. The features of prediction are collected from the statistical value in each bot. Therefore, the player knows which team (radiant or dire) is going to be the winning team. To predict the winning team, we use Naive Bayes (NB) as a classifier in data mining algorithm. NB is the appropriate algorithm. Since NB works on probability, it can predict the winning team, not only the winning heroes. However, NB has a shortcoming, e.g. imbalance data, this study proposes to implement NB+Adaboost. This study evaluates some approaches of NB, i.e. discretization and Gaussian distribution kernel function. Both are used to treat the numerical attribute. The results of the experiment show that the highest accuracy of the win prediction by using NB+Adaboost with Gaussian distribution kernel achieves 80%

title: Performance Evaluation of a Semantic Perception Classifier
abstract: In order to assess the technology developed by members of the U.S. Army Research Laboratory s Robotics Collaborative Technology Alliance (RCTA) RCTA s Integration and Assessment team conducts experiments to test the limits of developed technologies. We conducted such an assessment in October 2013 to evaluate the performance of a semantic labeling system developed by researchers at Carnegie Mellon University. The system consists of a Basler AC1300gc camera with Fujinon TF28DA8 lens and processing software, which runs on a laptop computer. The apparatus functions on small robotic platforms, such as a K bot. This report details the design for our data collection and our assessment of the technology by examining precision, recall, and F measure via exploratory data analysis and analysis of variance (ANOVA)

title: Evolving the cooperative behaviour in Unreal(tm) bots
abstract: This paper presents an approach to the evolution of the cooperative behaviour of some bots inside the PC game Unreal(tm) We intend to create bots that cooperate as a team trying to beat other teams (composed of human players or bots) So, in addition to the improvement of the default artificial intelligence (AI) of bots, we have performed an improvement of the 'team AI' We have applied an evolutionary algorithm which optimizes the parameters considered in the hard coded states inside the bot AI code, mainly those related to the cooperation. Two different approaches have been tested inside some different battle arenas: one considering a different set of parameters for every bot in the team, and the other one considering the same set of parameters for all the teammates. The results show that both methods yield better teams than the standard ones. The teams which share the same behaviour parameters, get a higher score than those with bots playing with different parameters.

title: Internal and contextual factors, knowledge processes and performance: From the Chinese provider's perspective
abstract: This paper explores the influences of two internal factors, i.e. supplier team's IT based skills and communication with client's team, and two contextual factors, i.e. supplier team's understanding of client's culture and collaboration with client's team, on knowledge processes and performance in global sourcing of IT services from the Chinese provider's perspective. Knowledge processes are characterized by knowledge sharing, knowledge based coordination and expertise management, and performance is measured by product success and personal satisfaction. Data have been collected in 13 companies in Xi'an Software Park, with 26 in depth, semi structured interviews held with top and middle managers, and 200 structured questionnaires distributed to knowledge workers who are involved in global sourcing projects. The results indicate that supplier team's IT based skills, communication with client's team, cultural understanding of client's culture and collaboration with client's team are positively associated with knowledge process and performance. Also, knowledge sharing, knowledge based coordination and expertise management are found to be crucial for those influential factors to function positively and contribute to the performance. The findings of this study suggest that the effects of key factors on knowledge processes and performance in global sourcing of IT services appear to transcend the social and cultural differences; however, contextual factors seem to have more significant influences on knowledge processes and performance in global sourcing of IT services.