a4da7ab7ca7aad33bafdbc28c57fac05f9d90c0b
[ker2014-2.git] / report / ass2-2.tex
1 \chapter{Visual representations and reasoning}
2 We chose pizzas for our domain, as everybody likes pizzas. We chose to have six different pizzas.
3 \begin{itemize}
4 \item Margherita; basilicum
5 \item Hawaii; ham, pineapple and basilicum
6 \item Salami; salami and basilicum
7 \item Funghi; mushrooms and ham
8 \item Pepperoni; salami and jalape\~nos
9 \end{itemize}
10
11 \iffalse
12
13 %% Margarita
14 p_margarita <- basilicum.
15
16 %% Hawaii
17 prob hawaiibasilicum: 0.1.
18 p_hawaii <- ham & pineapple & basilicum & hawaiibasilicum.
19
20 %% Salami
21 prob salamibasilicum: 0.1.
22 p_salami <- salami & basilicum & salamibasilicum.
23
24 %% Funghi
25 prob funghiham: 0.5.
26 p_funghi <- mushrooms & ham & funghiham.
27
28 %% Salami
29 p_pepperoni <- salami & jalapenos.
30
31 %% Oliva
32 p_oliva <- basilicum & olives.
33
34 TODO
35 In a more structured way, you are required to describe clearly and fully:
36 • The domain and the individual images
37 • The representation of observations, assumables and the causal theory between them in AILog
38 • The style of reasoning employed (abductive, deductive) and what considerations have played
39 a role
40 • The queries used to infer information about individual images
41 • The results of the queries and reflections on their effectiveness and intuitions behind them
42 • General reflections on your model, how appropriate it is for this domain, how effective it is
43 and how would need to proceed if one would like to extend it to large datasets or realistic,
44 real-time operation.
45
46
47 \fi