{"id":34773028,"url":"https://github.com/campiohe/breakthrough","last_synced_at":"2026-04-27T02:31:43.601Z","repository":{"id":129986378,"uuid":"203585971","full_name":"campiohe/breakthrough","owner":"campiohe","description":"SI202 : Resolução de Problemas I","archived":false,"fork":false,"pushed_at":"2019-12-01T18:05:06.000Z","size":96,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-12-26T19:42:55.996Z","etag":null,"topics":["analysis","chess-variant","data-science","reinforcement-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/campiohe.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-08-21T13:05:20.000Z","updated_at":"2021-03-01T12:16:10.000Z","dependencies_parsed_at":null,"dependency_job_id":"20805cf7-4121-4a7b-af0b-64fb01adb658","html_url":"https://github.com/campiohe/breakthrough","commit_stats":null,"previous_names":["campiohe/breakthrough"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/campiohe/breakthrough","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/campiohe%2Fbreakthrough","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/campiohe%2Fbreakthrough/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/campiohe%2Fbreakthrough/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/campiohe%2Fbreakthrough/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/campiohe","download_url":"https://codeload.github.com/campiohe/breakthrough/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/campiohe%2Fbreakthrough/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32320237,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-26T23:26:28.701Z","status":"online","status_checked_at":"2026-04-27T02:00:06.769Z","response_time":128,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","chess-variant","data-science","reinforcement-learning"],"created_at":"2025-12-25T08:04:04.606Z","updated_at":"2026-04-27T02:31:43.584Z","avatar_url":"https://github.com/campiohe.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# py_breakthrough\n SI202 : Resolução de Problemas I\n\n\n## Trabalho 1 (individual)\nCada aluno será responsável por analisar o jogo de breakthrough e criar 10 estratégias para verificar se consegue vencer a partida contra um adversário que joga ao acaso. As estratégias podem ser simples e baseadas simplesmente em heurísticas, o seu objetivo é garantir que entende bem o que funciona e o que não funciona no jogo. Por exemplo, algumas estratégias podem ser:\n\n1. Mover sempre o jogador da frente. \n1. Mover sempre o jogador de trás. \n1. Mover em blocos de jogadores. \n1. Capturar sempre que possível.\n\nO que você deverá fazer é implementar programas com alguma dessas estratégias e analisar esses programas. Você deverá implementar o seu programa em um kernel no jupyter e adicionar as suas análises no próprio kernel. Desse modo, ao compartilhar com o professor o seu kernel, ele poderá ver as suas análises e o seus códigos.\n\n\n## Players (strategies.py)\n* __dump_player__ : calculate the square values of the board, perform a move that tries to get a square with the highest value\n\n* __evil_player__ : calculates the game score by the evaluation function, performing the move that gets the highest score in that state\n\n* __forward_player__ : always move the piece that is in the rows with the highest index and try to capture whenever possible\n\n* __mirror_player__ : whenever possible, try to make the same move as your opponent\n\n* __team_player__ : moves a piece following the neighbors and capturing whenever possible\n\n* __killer_palyer__ : calculates the shortest distance for an opponent in order to eliminate it\n\n* __zigzag_player__ : moves only across the diagonals alternately, capturing whenever possible\n\n* __dodge_player__ : checks if the destination square is occupied and defended, if not defended moves to it\n\n* __conn_player__ : calculates the connectivity of the pieces by performing the movement that will assign a higher connectivity value\n\n* __sup_player__ : check if the destination house is defended by allies, if it is, performes the move\n\n### 100 match results\n![results](https://user-images.githubusercontent.com/37659078/64434318-adbc9580-d096-11e9-9ff3-1b888b7942d4.png)\n\n## Minimax \n- [x] alpha-beta pruning\n- [x] dynamic programming\n- [ ] improve evaluation function\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcampiohe%2Fbreakthrough","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcampiohe%2Fbreakthrough","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcampiohe%2Fbreakthrough/lists"}