From 1694b6ec59501b5ceb74e3df6d47fb340e923f8c Mon Sep 17 00:00:00 2001 From: Fabricio J Barth Date: Mon, 13 Nov 2023 07:26:06 -0300 Subject: [PATCH] avaliacao final 2023/2 --- docs/avaliacoes/2023_2_final/img/grafo.png | Bin 0 -> 52755 bytes docs/avaliacoes/2023_2_final/img/input.dot | 21 ++ .../2023_2_final/img/mapa_01.drawio | 124 ++++++++++ docs/avaliacoes/2023_2_final/img/mapa_01.png | Bin 0 -> 20510 bytes .../2023_2_final/img/mapa_01_01.drawio | 187 +++++++++++++++ .../2023_2_final/img/mapa_01_01.png | Bin 0 -> 30909 bytes .../2023_2_final/img/mapa_02.drawio | 160 +++++++++++++ docs/avaliacoes/2023_2_final/img/mapa_02.png | Bin 0 -> 27833 bytes .../2023_2_final/img/mapa_02_problema2.drawio | 163 +++++++++++++ .../2023_2_final/img/mapa_02_problema2.png | Bin 0 -> 28955 bytes .../2023_2_final/img/mapa_03.drawio | 226 ++++++++++++++++++ docs/avaliacoes/2023_2_final/img/mapa_03.png | Bin 0 -> 38490 bytes docs/avaliacoes/2023_2_final/index.md | 104 ++++++++ mkdocs.yml | 1 + 14 files changed, 986 insertions(+) create mode 100644 docs/avaliacoes/2023_2_final/img/grafo.png create mode 100644 docs/avaliacoes/2023_2_final/img/input.dot create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_01.drawio create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_01.png create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_01_01.drawio create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_01_01.png create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_02.drawio create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_02.png create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_02_problema2.drawio create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_02_problema2.png create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_03.drawio create mode 100644 docs/avaliacoes/2023_2_final/img/mapa_03.png create mode 100644 docs/avaliacoes/2023_2_final/index.md diff --git a/docs/avaliacoes/2023_2_final/img/grafo.png b/docs/avaliacoes/2023_2_final/img/grafo.png new file 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-0,0 +1,104 @@ +# Avaliação final 02/2023 + +## Orientações gerais + +* Quaisquer hipóteses relevantes devem ser **explicitamente formuladas**. Faz parte da avaliação a **correta interpretação** das questões. A **clareza** e a **objetividade** das respostas serão consideradas na avaliação. + +* Esta avaliação é **individual**. Em hipótese alguma você poderá fazer uso de material de colegas. + +* Durante esta avaliação você poderá fazer uso do seu material e de material existente na internet. + +## Questões + +## Grafo (2.0 pontos) + +Considere o grafo abaixo onde o número sobre as arestas é o custo de cada ação e o número em cada nodo é a heurística considerada até o +estado objetivo. Note que cada arco é bidirecional. Considere o estado **A** como inicial e o estado **G** como o estado objetivo. Em todos os algoritmos utilizados existe um único tipo de poda da árvore de busca que é: não pode-se abrir nenhum sucessor igual ao pai do nodo. + + + +* Qual é o caminho para a solução do problema que o algoritmo A* encontra. + +* A heurística utilizada no problema é admissível? Explique. + +* Qual é o caminho para a solução do problema que o algoritmo Ganancioso encontra. + +* Qual é o caminho para a solução do problema que o algoritmo de busca em largura encontra. + +* A solução encontrada pelo algoritmo de busca em largura é ótima? Justifique a sua resposta. + +Cada item desta questão vale 0.4 pontos. No entanto, cada item errado anula um item certo. Ou seja, se você não tiver certeza sobre a resposta, é melhor deixar o item em branco. + +## Bicicleta na montanha (4.0 pontos) + +Neste exercício você deverá implementar um agente que é capaz de ir de um lugar até outro em um mapa. Este agente está andando de bicicleta e sabe executar as seguintes ações: + +* ir para baixo; +* ir para cima; +* ir para esquerda, e; +* ir para direita; + +Um exemplo possível de mapa que este agente pode receber é apresentado abaixo: + +Mapa 01: + + + +Neste estado inicial: + +* o ciclista está na posição [0,0], +* o fim do pedal mais o almoço estão esperando o ciclista na posição [0,5], +* o mapa tem 5 linhas e 7 colunas, e +* existem campos no mapa que estão pintados em cinza e que representam terrenos que tem uma altimetria mais alta. Ou seja, o ciclista terá que gastar mais energia para passar por estes campos. No caso dos campos marcados em cinza, o custo de cada ação é 2. + +Cada ação que o agente sabe executar tem custo 1. No entanto, quando uma ação é executada em um campo com altimetria maior então o custo dela passa a ser 2 ou 4 - dependendendo da altimetria. Se o campo tiver a cor cinza então o custo é 2, se o campo tiver a cor preta então o custo é 4 e se o campo tiver a cor branca então o custo é 1. + +Implemente uma solução que ajuda o ciclista a tomar a decisão com relação ao melhor plano - aquele plano que faz ele chegar no objetivo com o menor custo. + +A solução implementada precisa ser capaz de tratar diversas configurações com diversas dimensões. Por exemplo: + +Mapa 02: + + + +Mapa 03: + + + +Mapa 04: + + + +Não existem testes pré-configurados. No entanto, recomenda-se fortemenete que você teste a sua implementação nos cenários descritos acima. + +Nesta implementação você deve utilizar a biblioteca `aigyminsper`. + +## Bike na montanha v2 (4.0 pontos) + +Considere o mesmo problema da Bike. No entanto, agora existem mais de um ponto com lanche e o ciclista tem um limite de energia. Ou seja, ele precisa chegar no ponto de lanche com energia suficiente. O custo da solução precisa ser o menor possível, mas também precisa ser menor ou igual ao limite de energia. + +Veja um exemplo de mapa abaixo: + + + +O seu programa deverá retornar o caminho que o ciclista deve seguir para chegar no ponto de lanche com energia suficiente. Sempre deve retornar o caminho mais curto, ou seja, o lanche mais curto. Se não existir um caminho que atenda a restrição de energia então o programa deve retornar `Sem solução`. + +Nesta implementação você também deverá usar a biblioteca `aigyminsper` e poderá usar como base a implementação da questão anterior. Mas, você deverá entregar dois arquivos distintos. + +## Entrega dos itens da avaliação + +Toda a prova deve ser submetida no Github Classroom através deste link: [https://classroom.github.com/a/fSGT_Ia-](https://classroom.github.com/a/fSGT_Ia-). + +Os arquivos que precisam ser submetidos são: + +* `Grafo.md`: onde você irá colocar a resposta para a questão do Grafo. +* `requirements.txt`: arquivo com todas as bibliotecas necessárias para as soluções que você irá entregar. +* `Bike.py`: arquivo com a solução para o problema da Bike. +* `Bike_v2.py`: arquivo com a solução para o problema da Bike na versão 2. + +Não existe nenhum projeto de *template* e também não existem arquivos de testes. + +No caso das implementações, você deverá definir tudo: como os estados são representados, quais são os parâmetros do construtor da classe, quais são as ações e como elas afetam os estados, qual é o critério que retorna `true` para o método `is_goal()`, e assim em diante. + +O horário limite para entrega da prova é 09:45 do dia 13 de novembro de 2023. Para fins de avaliação será considerado último `commit` no repositório. + diff --git a/mkdocs.yml b/mkdocs.yml index db7797f2..d7e37ce0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -59,6 +59,7 @@ nav: - 'referencias/06_rl/index.md' - 'Avaliações': - 'avaliacoes/2023_2_intermediaria/index.md' + - 'avaliacoes/2023_2_final/index.md' - 'Projetos': - 'projetos/11_cavalo/index.md' - 'projetos/15_rl/index.md'