lina994 / ai_ass4

Probabilistic reasoning using Bayes networks, with scenarios similar to the hurricane evacuation problem environment. Introduction to Artificial Intelligence 2019 Ben-Gurion University.

Hurricane Evacuation Problem: Locate the Blockages and Evacuees

Probabilistic reasoning using Bayes networks, with scenarios similar to the hurricane evacuation problem environment of assignment 1.

Using the following principles:

• Artificial Intelligence:
  • Reasoning under uncertainty
  • Bayesian Networks
  • Enumeration (Enumeration Ask and Enumerate All)
  • Probability Reasoning
• Topological sort
• OOP, Files I/O

Domain Description

• Binary-valued occupation of vertices (contains people to be evacuated, or not).

• Binary random variable Fl(v) standing in for "flooding" at vertex v.

• Binary random variable Ev(v) standing in for "people to evacuate" at each vertex v.

• Binary variable B(e) standing in for "blocked" for each edge e.

• The flooding events are assumed independent, with known distributions.

• The blockages are noisy-or distributed given the flooding at incident vertices, with

  • pi = (1 - qi) = 0.6 * 1/w(e).

• There are people at vertex v, with noisy or distributions given all the edge blockages at all edges incident on v, with:

  • pi = (1 - qi) = 0.8 for an edge with weight greater then 4.
  • pi = (1 - qi) = 0.4 for shorter edges.

• All noisy-or node have a leakage probability of 0.001, that is, they are true with probability 0.001 when all the causes are inactive.

types of BN nodes

• Blockages (one for each edge).
• Flooding (one for each vertex).
• Evacuees present (one for each vertex).

Running

Input

file.txt file include graph description and parameters such as P(Fl(v)=true)) For example:

#T 4           ; number of vertices n in graph (from 1 to n)
#V 1 F 0.2     ; Vertex 1, probability flooding 0.2
#V 2 F 0.4     ; Vertex 2, probability flooding 0.4
#E1 1 2 W1     ; Edge1 between vertices 1 and 2, weight 1
#E2 2 3 W3     ; Edge2 between vertices 2 and 3, weight 3
#E3 3 4 W3     ; Edge3 between vertices 3 and 4, weight 3
#E4 2 4 W4     ; Edge4 between vertices 2 and 4, weight 4

Graph visualization

We will get the following Bayesian network:

Additional input will be provided by the user via the terminal:

• Locations where flooding, blockages, or evacuees are reported either present or absent.
• Reset evidence list to empty.

Once evidence is instantiated, you can perform reasoning about:

• Probability that each of the vertices contains evacuees.
• Probability that each of the vertices is flooded.
• Probability that each of the edges is blocked.
• Probability that a certain path is free from blockages.
• Path from a given location to a goal that has the highest probability of being free from blockages.

For example:

Please enter an action:
'e' for adding edge evidence
'v' for adding vertex evidence
'q' for quit
'pr' for Probability Reasoning
'reset' for reset evidence list
'printEv' for printing the evidence list
> v
Please enter vertex number (starting from 1)
> 1
Please enter the number of the report:
1 - Flooding
2 - no Flooding
3 - Evacuees
4 - no Evacuees
> 1
Please enter an action:
'e' for adding edge evidence
'v' for adding vertex evidence
'q' for quit
'pr' for Probability Reasoning
'reset' for reset evidence list
'printEv' for printing the evidence list
> pr
Please enter an action:
1 for print probability for each of the vertices (enumeration)
2 for print posterior probabilities for each of the vertices ()
3 for print probability of path
4 for print path with highest probability
> 1
...
Please enter an action:
'e' for adding edge evidence
'v' for adding vertex evidence
'q' for quit
'pr' for Probability Reasoning
'reset' for reset evidence list
'printEv' for printing the evidence list
> q

Output

• Detailed output will be displayed in the terminal
• A summary will be saved in the results.txt file

Authors

• Alina
  • github
• Elina
  • github

Official assignment description

assignment 4