{ "cells": [ { "cell_type": "markdown", "id": "f9a330b8", "metadata": { "tags": [ "CSP", "single", "easy", "Extension" ] }, "source": [ "# Problem *Traffic Lights*" ] }, { "cell_type": "markdown", "id": "68037a71", "metadata": {}, "source": [ "From [CSPLib (Problem 16)](https://www.csplib.org/Problems/prob016/):\n", "Consider a four way traffic junction with eight traffic lights.\n", "Four of the traffic lights are for the vehicles and can be represented by the variables $v_0$ to $v_3$ with domains {r,ry,g,y} (for red, red-yellow, green and yellow).\n", "The other four traffic lights are for the pedestrians and can be represented by the variables $p_0$ to $p_3$ with domains {r,g}.\n", " The constraints on these variables can be modeled by quaternary constraints on $(v_i, p_i, v_j, p_j)$ for $0 \\leq i < 4, j=(1+i)\\, \\mathtt{mod}\\, 4$ which allow just the tuples {(r,r,g,g), (ry,r,y,r), (g,g,r,r), (y,r,ry,r)}.''" ] }, { "attachments": { "trafficlight.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "61ba459d", "metadata": {}, "source": [ "How to adjust traffic lights? Image from [freesvg.org](https://freesvg.org/traffic-lights-selection-vector-image)\n", "" ] }, { "cell_type": "markdown", "id": "da9c9768", "metadata": {}, "source": [ "To build a CSP (Constraint Satisfaction Problem) model, we need first to import the library PyCSP$^3$:" ] }, { "cell_type": "code", "execution_count": 1, "id": "7a90a916", "metadata": {}, "outputs": [], "source": [ "from pycsp3 import *" ] }, { "cell_type": "markdown", "id": "5c8f607c", "metadata": {}, "source": [ "Then, we need some data. Actually, we just need a few constants." ] }, { "cell_type": "code", "execution_count": 2, "id": "134e1f5c", "metadata": {}, "outputs": [], "source": [ "R, RY, G, Y = \"red\", \"red-yellow\", \"green\", \"yellow\"" ] }, { "cell_type": "markdown", "id": "d930d24e", "metadata": {}, "source": [ "We start our CSP model by introducing an array $v$ of 4 variables, one per vehicle traffic light." ] }, { "cell_type": "code", "execution_count": 3, "id": "ced13527", "metadata": {}, "outputs": [], "source": [ "# v[i] is the color for the ith vehicle traffic light\n", "v = VarArray(size=4, dom={R, RY, G, Y})" ] }, { "cell_type": "markdown", "id": "7453d270", "metadata": {}, "source": [ "We introduce a second array $p$ of 4 variables, one per pedestrian traffic light." ] }, { "cell_type": "code", "execution_count": 4, "id": "a6d22175", "metadata": {}, "outputs": [], "source": [ "# p[i] is the color for the ith pedestrian traffic light\n", "p = VarArray(size=4, dom={R, G})" ] }, { "cell_type": "markdown", "id": "96dbe2e0", "metadata": {}, "source": [ "We can display the structure of the arrays, as well as the domain of the first variable (note that all variables have the same domain)." ] }, { "cell_type": "code", "execution_count": 5, "id": "6a9b9cbe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Array v: [v[0], v[1], v[2], v[3]]\n", "Array p: [p[0], p[1], p[2], p[3]]\n", "Domain of any variable: green red red-yellow yellow\n" ] } ], "source": [ "print(\"Array v: \", v)\n", "print(\"Array p: \", p)\n", "print(\"Domain of any variable: \", v[0].dom)" ] }, { "cell_type": "markdown", "id": "6863af29", "metadata": {}, "source": [ "As indicated in the statement of the problem, we need a table specifying the right combinations of values." ] }, { "cell_type": "code", "execution_count": 6, "id": "fa13ff6f", "metadata": {}, "outputs": [], "source": [ "table = {(R, R, G, G), (RY, R, Y, R), (G, G, R, R), (Y, R, RY, R)}" ] }, { "cell_type": "markdown", "id": "e4735d87", "metadata": {}, "source": [ "We can then post 4 quaternary constraints *Extension* by using the table." ] }, { "cell_type": "code", "execution_count": 7, "id": "8250664c", "metadata": {}, "outputs": [], "source": [ "satisfy(\n", " (v[i], p[i], v[(i + 1) % 4], p[(i + 1) % 4]) in table for i in range(4)\n", ");" ] }, { "cell_type": "markdown", "id": "0440c33b", "metadata": {}, "source": [ "We can display the internal representation of the two posted constraints; this way, although a little bit technical, we can see that the arguments of teh four constraints are correct." ] }, { "cell_type": "code", "execution_count": 8, "id": "317d2981", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "extension(list:[v[0], p[0], v[1], p[1]], supports:(green,green,red,red)(red,red,green,green)(red-yellow,red,yellow,red)(yellow,red,red-yellow,red))\n", "extension(list:[v[1], p[1], v[2], p[2]], supports:(green,green,red,red)(red,red,green,green)(red-yellow,red,yellow,red)(yellow,red,red-yellow,red))\n", "extension(list:[v[2], p[2], v[3], p[3]], supports:(green,green,red,red)(red,red,green,green)(red-yellow,red,yellow,red)(yellow,red,red-yellow,red))\n", "extension(list:[v[3], p[3], v[0], p[0]], supports:(green,green,red,red)(red,red,green,green)(red-yellow,red,yellow,red)(yellow,red,red-yellow,red))\n" ] } ], "source": [ "print(posted())" ] }, { "cell_type": "markdown", "id": "832c94e8", "metadata": {}, "source": [ "Interestingly, by calling the function *solve()*, we can check that the problem is satisfiable (SAT). We can also display the found solution. Here, we call the function *values()* that collects the values assigned to a specified list of variables." ] }, { "cell_type": "code", "execution_count": 9, "id": "a63b47db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Colors for vehicles : ['green', 'red', 'green', 'red']\n", "Colors fo pedestrians: ['green', 'red', 'green', 'red']\n" ] } ], "source": [ "if solve() is SAT:\n", " print(\"Colors for vehicles : \", values(v))\n", " print(\"Colors fo pedestrians: \", values(p))\n", " " ] }, { "cell_type": "markdown", "id": "2361fcbb", "metadata": {}, "source": [ "We can enumerate the solutions as follows:" ] }, { "cell_type": "code", "execution_count": 10, "id": "85511b66", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution 1: ['green', 'red', 'green', 'red'] ['green', 'red', 'green', 'red']\n", "Solution 2: ['red-yellow', 'yellow', 'red-yellow', 'yellow'] ['red', 'red', 'red', 'red']\n", "Solution 3: ['yellow', 'red-yellow', 'yellow', 'red-yellow'] ['red', 'red', 'red', 'red']\n", "Solution 4: ['red', 'green', 'red', 'green'] ['red', 'green', 'red', 'green']\n" ] } ], "source": [ "if solve(sols=10) is SAT:\n", " for i in range(n_solutions()):\n", " print(f\"Solution {i+1}: {values(v, sol=i)} {values(p, sol=i)}\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 5 }