Hill Climbing.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "# Hill Climbing Search",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Hill Climbing is a heuristic search used for mathematical optimization problems in the field of Artificial Intelligence.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "In examining a search tree, hill climbing will move to the first successor node that is “better” than the current node—in other words, the first node that it comes across with a heuristic value lower than that of the current node. Now hill climbing proceeds as with depth-first search, but at each step, the new nodes to be added to the queue are sorted into order of distance from the goal.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![HCS.png](attachment:HCS.png)",
      "metadata": {},
      "attachments": {
        "HCS.png": {
          "image/png": 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fJ+4p1BjbH0z/7OmsweuJ2x/MbiMUn7zf3YCTbW7ovo83guLNVqFvurHZI2Ouu34/tIp214OD5/jEYZhrviW1xHA3WGmhqYyQS45yOyRXNmpujYnX8wuiw8Ng41lfSafck4QamCG55LtG7qho+T5X9ZmWv3x1i7fBTO3cVvp5UsuQFiGthhAUTZQyGJRehXcNorhl0qBXV6xTpsb296qKRDdUW4T4tMekJpDa5f2F68aoVHVllmnVdfM5LYVUEjH/bbI3lqtyN7zhuEr5Uwxaj6QxqpJ3nUqGnBVkAz2Kz/zw3QR2QcAo5R+xy6+WWbTLznsdNlHfUi7hh+/nSWwxKquq6vufRLiLm7hKvIg3oLfej7DcRe63mi45p9b5zeutnhl2F29VN9XeneL3mmp6FtEsNGkRYvtT6w3/Ciuyl1Bza0xEvH+0GIpqBn8+vM2G2IiyROL9xkENVCK9U4nRvjMGA7bKSGALAcHakKAsMXoZym69Wg2hdiVU+hdioyuqhSwdZWxYp+Q3VGuE2GgB+4WiZHzu6XfBtOGHsrdCyzKlunKaq5JfGdj9ohy7SqC1uR+eBNO2Csrypxu0HrQx5kzedQrnxFhudz6Zk2it0guRE+ZPHdUWg7m8qtSU6VHjuyL2k8yHnydx/Ckm21BQPDoxFV2bEl1UGJOzYVet6SPB4Vc0nscmQelwVUi1llpoSn4oRPiWYtel6ta4HfO9bzGUtHleHIJjBA/6fbjzktyThBpIQVqJcTaL+GSLDAwIilahUoayVqFSQ/ahU2JrY6pbptoEjbK6Um6o5gjxg8dY2u58MiPpC8urOgc1y4rqiluWMoU0sJKeLcWtcU1yZXn5qxi0HkVjpHME81Pc6OYfPY02RjJmUQzv3zzuslR3PQzHt6O+ztLMv1KVgD9KPcdEqHWU9uHnieOU3GPJRYWkboobm+gEeSOkgyORWWhKfigE0qwj8b+ETBDy6NaIHyoVd8ZlewlpgTSUOlXRN4q9RiU55VDolPwEAWmEajVEs05JrI35iwpFysN3TKgoqqvaDRWi4lGPEK2G7t9sLDVJhoepePgjDD3LiuqKUaaQDp7Q6GkuZSTDNEX5qxi0ylOILyrklSdvVfPI5tETS8+/dFirU49lebGKSkr6L3nR4OfGYft5Ku6x5LmxR39KcY+piC6RypHW/hTp5x+JW+PBPmKaRqlTwo2TF9GBdEp0jux+adMptBTorbE4vNxYng/A5a+0kKWjqCFqN1RrhAR28bnrPSvfM8KHmRLjUcmyMjEc1EA1ELs07nKUpe2J5OUvSonkX/IU5qMx0sm/Tm1N+74kgz522lVf1jH8ioTKvRCxha7xD67wxi4bdjeZyeidnKjwk8yDn6fyHq9PORutpE5L56cqiO2kZH4KT8CZPhxe2YoH3XceRVQqCqVaUyOkzU+puTXy4770JG7OcIJ4TvG9UmognYPrlEoZSiJUrSGadEphbcxBccsU/qNAad2pdkO1RIi2Qv/+pZ8b9CHu4Vdv/GB4hdxQRVVXyTJVkqjmokrWQ777fjzo2wi7mo0dw8T8WFH+agatR9EY6eR/furNf9zFA12O1Ps+Eiz3QkSrU653LdxZTE3HJx+3WN9JOVUq/CQP38+TcuO1ve8jsL897j2PE5jtFQ+tWu/xvu/yu5es/E9U3Br5eXTu1CzvGbWDJbKC+GGKoAaqcXCdUilDaYSqNUSLTnFRicdk/IiMaiFLRVFd1W6opgiTb8b+jmedOFLv+wiUqk7LMlWnlCmkwdWuu3+slrzvI8jLX82g9QgaI50jG/cBeQCtzdxrN6UHDjmxOj7QpPheKTUQAI4a0KmTS3LJ12Gp7vxs7DDMh/H3SruEAUgaaiAAHDmgUwBHLh8xBYC8Azp1uKQXUovR/3cfEfs6PBGVvUSnBgLAMQA6BQCA3gGdAgBA74BOAQCgd0CnAADQO6dBpxKx8W+934Xe5mMV0HZ09OG/PQmrLe8EAOAQOBadoi6oxSgcaZV2RfsFbYddDcaGgbGVvEgJWhntr2UaXGHVpcwAAOSKvnSK4kjLodxmoR1iZGHr/TGz00qrxapW8CYsLW7fAAAcFJ3pFJUcdIp00C5Kvj6Qi+pRQS99juK9rX8AADgoedYp7BYmbEbFoHlPU3VPIMLplMKzVX2IJ1cW7VanxMcr491FtVil+g5HMjtgM1fH5mE0V1k25nUwRTcD6zD0A4C8kGedwl6UZR3DkYjnqrHZE9kc7Ttz2T23zOmU3LOVh9rZkQbux+oUu/3KezrKSxCDJ6mR6/+h6JS6qyy2xUgbOQIAcNjke9zH++T+8H1XqcH0kX/6Hy243/FfWKeknq3C6Xvr1L6sTrGbh2yASbO4VRq5zit1KpurLD5HlAsAAA6VfOsUlpWi7ls3yy+1NF/sG/jAjN3/8PyUzLOVP1uDTuHfCgM3nmx75yg6JfdCwuA4pd2ujPMRinqayNXxD4VL8ohcZXEKs7jWAgCQE/nWKZQY7T9jMDD2b565r5wxnzFjeRLNo+9bp/ZldUpETeqgpqZT0rC0TpEuG7l6FldZ8skgRbIBADgk8q1TfH/EhP3F8SQO/zm2XHRqX1anyZXhD4zSUR7N4lapU3i4WuOc3GZnPK1WAy9Pqq6yWxHPVabCGQKbJgDID3nXKfIdN/LdETynww+OctEpTqn2YXVK5ulLsKOzEMBdUWlxq9QptBm+h31XmQqH6+6Ns9UkhSqusjsv3BesWNTILwEAOHTyr1PHC1r0d5cb27zCt5AOn+SK/4bV8r4vBjIFAPmi0HWK3zfDkDUQQsghgtjYt90222F+6RMAAAUFr1MAAJx4QKcAANA7oFMAAOgd0CkAAPQO6BQAAHon7zqFln64WX1G9x+GAgBAv+Rbp/CKcAu/lQ8AAOBA5Fun2JjXLl3qDQAAsD9ApwAA0Dv51qntqOcqTE4BAJALedWpnfi4s8nWOBgE63AAAA7OEehUiyu8IQQAAADsnyMY971T4ZyEcR8AAAcG5tEBANA7oFMAAOidfOsUrPMEACBX8q1TsG8GAIBcybtOAQAA5AjoFAAAegd0CgAAvQM6BQCA3gGdAgBA74BOnRDY2Pij//ldKB9f96KyGR199PBJOH5U1wOALBSETuEvEputPSOS7c5odcp9zYa/mlzc7J5W/75eYtZ9hbF+HBB92z3/LPjs5LvKyo8/09kIu/5grPlkbO3IVnck10ZvlzF/gL2ZgB4ojP7U+uzwva/HF8XPfhTxNJvsnsgmu/Tri+UsWoA2Z793fT32+ug7DtSP1NNAC942Y3VvYClrGl/7u8prXdN7ye3Omv8jU60rvKcsozlPs0Wu/gBwHBTsuI8dHzDrfMOOVp0iPb6au6Ht7FIa9baUtnijwpEqeCeTocUbEw6zwC76Ooymj/xH2tMEAAonXaeSS75reGxnMGeaaDI4WELCeLKshV/y2fkTM+0WsbHH/fVkUGZu6Pb+simEK9ie+ef75211jfVVxWeKbgbWiYig1dD963VmLlJrXfc3oTi+MHr9Y38didBy+c6zVxmx0ahT6Fd3g0XUUaKkEOcYH/NY7b4FcuKrQP9FMw4523TnKe6MSUuGsfuWSIy77MuRwXfwGJmpcLgn0nNSKOZtZcp6AivCMQAcE4XRn6J0JTT3p2T9i42Qs6Go27+KkvG5p98Fl1X6MNsL3vaiNu8C2kXxn4cu9JJOB9qaultLBpu77IyntbTWObHFncuuxJYTXLfopfeaZMClUacSo31nSrv8r4VD1RQqCyGxHFthOV17+dBhanSFeclV9qdwXkz1nwbjSXbB12mtHwzGhf8kJ50V5gb3ryqFAABHBOiUrN3iRmssbXc+mcn6quu1v6uyyTNPTuGuXtEx/Gp3dzPsajR1+cmETnoaiOv+/DjkqDUbLJVVJYw4SRp1Cp9WNTCe0g61FO5Mu2rLRXLG5ezlyNB71WajubKylEnHIE4kAffXrCkxWgn0VIr6blzW9D56Bk4DoFOK/gUeu91sLDUxtmvuqVUVsRJfMf13fHygKn3NVAIW/V0Vtq6Hs/GNV74Pig5Bp1RSiK8nPi35xn/dYvvvD2fXtl896ixK/0uSSAz+oTASJDCigTKXNQvsIQeOHdAplXlldvG56z1r8Z9GN6hKJe53pK9O609tZeRDniSNOkV+JssdRpZCuU6J9EjyL4VO4Y6YNdU3lILnsyyUSwPA0QI6JdMptBX69y/93JAKsWFXvfGD4RVqZyK54r9hLfu//ItrS8GvO21WcnUyP2Xt8Irnp/Db/ZIGV2h7d3PW3coUXfevpiLUqFO7r4Y7zopGaiopxHNJts7h31H858/vfLvAJiKeq8YGV3gbsbP3mhhbt59f1oCnt0ydwyvobfDzvz1aSOzurk85L5lavvolntxF8cjTwMQbIYV4bQfT4AzBEirgmAGdkulU8s3Y3x3VFjwAyv6+Dy2OfdZZbS6ytXa8W1ksXJ3yvi8Zn/qy1cKFFFV1/EtfS2WzZ07ouWjVKX59wIcpxVRLYSL2+Fa16J0dik+4WvFrQKaq45O+FmuzJ4IvjNjY973VRQaDyea4N8W/kYxPP+zl3wyaShtv+eZ4YdK4HgIA8k5h6NQxgOIvnj6dxZ2aBV9ncXPqbVpeQCuPu63FbV6iM0cFuait3RcFlQKOHdCpg4Hf4g22luPFSOaLvQ+n87wPDu+bYcpujx7Zvhk2OtxdbWnzzEJnCtABoFMAAOgd0Km9SK9ZFwPv6gHgCAGdAgBA74BOAQCgd0CnAADQO6BTAADoHdCpA6J5HekJJRmfuuewmQwGxlz9wT28i1C84Ua0ODbtm8qUtw7+GGPxOgYUn3A7KnCY7Z3BkZfkJ1Fvi2gbIXPNt5TM/NZgqe78OrXoVPitwXyu817GZAY4zYBOaUTulnmsOqXZk/PAbPznQNm5Xv8Ci9ZmfR83vPfPGKLq1M5a4GMrWdhFPGEqewLLu7vLgZ6qsv5nayix4PvQKng6K/cMoI3xO2V471ECxV/4eq68h9eUro4P1JX1Pl5kk/FZX09Dly+mbhkNnBpApzQib2bHqlMqe6cPEbyn57J7LiEcYqg6JZbv9bnAI/yliTV/V9ruanMu8PBx6C3XUVLqFImkwZ3aRsTDnXYWHK8AGaBTe0N1y8Q6Zbp47b0aPLqp7h9Z4hUrERsZasXDpfTuOa7h2a785XPiwFlUffMHFZNztB26W2NsfzD9s6eTi9Nka39A1oIrIqR6cma2CqYVhBphRFtiuOREvG3FlqZPRmJpqaLpFPauqpIZfqI5d0Pa4DSDUqd43/ezTQM/8KNFAnHXslweEEaLAIABndIIrT9lMJLRzUufw8Z3AXDDMzUOBd8iNuLrrCgffL61uzraV2Ew1PaPrqBFn8Mo66SIIP4qlVVV9f1PIvNeh+ncYHCdFiGHoj9F0SlqhAtaE4M3Bj0daikxZCSDplO4FMRmMhiVniZFp7AKP/u0xWKUCCK78GyozWKQ6RdwqgGd0obCLZPoFN/O0w04Mee+nBrIoPXAzSI8hYT/KwTSWrWIlUBPmYH3k0Lx6MRUNL5Bi5D7W+HJSdUpSoSrmhNDYF+Nuf5oY8o6fRH2EHQqNY/OT6ILIHbpP1yOCsb6oQ+bzPAklsZcDluRtdO3AFIFgE5pRdEgccCZ/tEE14rSDZhIkhi8vQYHnukbxU1wD2nAZ0rUhx6hEC6RgoxObUc9bSmdokeoLTFpNiMeuxGL3X+JLprSKSzflbJx307YVatt3JcB23UVF/c9EzldcT3I+63Gqr7Rt0IAcIoBndIGVaeEVptWDdzNMTR5pE4oIk3RoFMS9aFHyKE4M6NTpA8l0ilphNoTk1x/uyb8UIh8NTh4rqgnsI6DcM+RTI2J59HZ5XAwFFuXzKOj5fD4dGydOo/O/Xfj7SoZyIq6gWh9dVXoQ2WVNuA0ATqlDblbJqLpFP+9mauuX9bQbjIe+enJxLJk2dG+dYoaIYfCk3M90FN00RlaJ30QQ0raDq5TKPbP9vL/dn+Guy5eW1B8wT27Q5YgkNEZio8P1Z+94H6xk/E15QNr8boEtOjvrhTWFgQ/rS9WW5ewHfP9t/KOf8zEk7t49q0aR4iivvaGjvu/pIy9Wtyz6cEgcHoBndKI3C2TplNc+14NP/y4Hvt5GpjS5l7fzHZOOkWNEIcqPDnjYbfdwp1ju+b6ovusMmECmhOTMSZlzHXX74fI1yLSn/kzWOv7HwuT3Jm1mplA0VrNS/3+30gCRPNTGPKWID55v7uBhIrsT0PfdPOfOzQ3dN+fhHWeAAfoFAAAegd0CgAAvQM6BQCA3gGdAgBA74BOAQCgd0CnAADQO6BTAADoHdApAAD0DugUAAB6pyB1asFnJwuahS1vAACcbAq3P5XZmgsAwMkGdAoAAL1z8nUqvTk2tUNYQK5TiZj/NtnQy5jrbnjD/P5bpU0wAAC646TrFLbTNtV/Gozz3zupHwymDABkOoWNWcq7/UsIrc398CT4BkuSiqsvAAD64oTrFP6OgDX1eZKVQE9l5tNVMp3CHyY4W2p3PplbS3W5qDbB/L8AANARJ1ynsIkSebMnwAjGvByK+SnsdtTTXMpkPJvG6a6+AADoixOuU+STKk2eeYqZGn0eHbFL4y5HGbHiVnP1BQBAX5z0+an1KeclU8tXv3D9IxSPPA1MkIknjFyn1kO++3486NsIu5qNHcMrqq6+AADoi5OuU5w6TT/svUgGf6bSxlu+udQnS+TzU8tjd/9YLXvfR3f1BQBAX5x4nQIAoOABnQIAQO+ATgEAoHdApwAA0DugUwAA6B3QKQAA9A7oFAAAegd0CgAAvVMYOsUujfRXM+cGg+tCAAAABURh6NSr4Y6GvtFV4QgAgMKiMHQq6m2pGxhPOU8BAFBYgE4BAKB3CkKn0Lyn6QJMTgFAoVIAOvVm/G+tttqh4AaYsgBAYVIoOnXBFd4GnQKAwqRgxn1NzlDKeQoAgMIC5tEBANA7oFMAAOidwtApWOcJAIVMYegU7JsBgEKmMHQKAIBCBnQKAAC9AzoFAIDeAZ0CAEDvgE4BAKB3QKf0Bxsbf/Q/vwutnKJ9QEed5c3o6KOHT8Jx8fW2o6MP/+1JeBW2X+kQ0Cm9sRF2/cFY88nYWlIIKHyOPsvJtdHbZcwfXGHRXiu0MtpfyzTARlE9Ajp1BOys+T8y1brCO8JxFtCCt81Y3RtYOj1tRVuWX/u7ymtd03sX4Zq/y9ToCm8Kh2qgOU+zxdozsiYcY1DE08xU9ASWhWNAN4BOHQFszGs3tHhjwmEWErPuK0zN3dApeqRrzHLU21La4o0KR1mIeVsMdm+MFQ5VYRd9HUbTR/41kfShlz5HsanLLxYvQA+ATmkhEfPfrjczBgNjrrvhDZONhGg1dP96HQ601nV/E4pzYxa0HbpbY2x/MP2zp7OGMZhs7Q9mN58PlnDnCDB23xKJkQ761d1gkfQa2N/8/ZfM+KfcVbzhTa4xi1qsqE2i+OT97gb+zPo//Ug6J8l46JvuOisOM1/6U+B3HMa+HBl8x4azUuFwT5AJGsTGHvfX86c1dHt/IV0RjVleDzkvGtu+mZ70dFYVcXG2P/h1m/pbNeRZpiQmGRwswcc8VrtvgZzJvg78mSSGsTQNPlviCmE9OHhOOIuDueZbIgNJSpYxKOZtZcp6AivCMYZ7ojiYopuB9dRJGMS+jvyGMwscG6BTGkhOOivKu/1LCK3N/fAk+AZL0tbU3VqT3RPZ3GVnPK2ltc6JLe7MnWlXrbWyqqq+/0lk3usw8Vt5NPenEqN9Z0q7/K+FQ+7KIWdF0XX/ahLFZ374buINbj5UnVodH6izdX8bYxG74OusHgpyadz4z4Gymu7hKLubWPB1VQ9yYdsL3nZT/afBeBKfZq0fDHIishFyNhR1+1dRMj739LvgMr6I5izvhF21xrKqyqb+kZl57zVTyWBwS/lbdeRZpiUGo+xPIXb51TKLdlmuoG0ZpZP3p6hZJuA8mhvcv4o1CRe44bJ7LiEccxmMPLhqYZgaMGI8TkCnNIAi3razpXbnk7m1VFXdDLsaUwME8fTTSqCnzFD8p1GuTqN4dGIqip/D4pOzgttYldj4gczdlOMrZ57nNJ1aD/QUNUgduNB64GZRhTMkVgncebGmWiaX1ErSvHFLNpa2O5/MZN5/ac8yvjRT3PeMuzbO8UQ0vqP8rTryLNMSw4EfAOViBcc9vpFPHdUWg7m8qtRkHhjnlQnrpng0R88yD1eS5vQPBZS3IPbPdovR0uaZhfn14wN0ShN4VNXTXMqYbI57U1gy4uMDVekqzo4PpOo7DldIkuTkbCgaCfeAj4f+0dNoYzJjloxOoainideptGBloHXicEKFURGB4Xo/WMfwgO5mY6mJsV1zTwkv5rVmGf8l6QNyKH6rjjLLtMSQq0hPe/O4y1Ld9TAc3476Oktp94KglmUMV5IW0SEBpwc2tOsO0CntIHZp3OUoI30Htf4UVZI06xRpZNLRDU9i6fmXDmt13+hbkU6RHhMvT5SXXLSXjGRY2uSZp3cM2MXnrvesfGdQQEOWlQoiIP6tOmpZliVGcRXyO75MVQSUJ0uW8byXRXZpMhcmU3zg+AGd0sJ6yHffj0cxG2FXs7FjeIWfrLF2eGXzU3RJwnMups7hFfQ2+PnfHi1k5j4UvBruOCvpjm1N+74kgz522lVf1jH8ih+81Dgnt/F1rQZDmye6zQ1Ohjur6geeLrGcOvz02c0vg/EkWvq209o48Ox3lpO5sf/35mc/x9H6lPOSqeWrX7gIUTzyNDDBTzyF/v1LPzfOQmzYVW/8YHiFC9ScZYpOKX+bBVmWqYnh55JsncO/o/jPn9/5doFFeA2BkSyAYsPuJjOZ0iInhpwVpg+HV7biQfedRxFuSEzLMoasQpANlrcinquMdLBMxn0MU90/gqfqgeMBdEoDaHns7h+r9375xUHVKcTGvu+tLjIY9hwH8S/LuWaWaktv/uOuo1b6vg9thu+1Whj89sp198bZaqIR3CV+HGwt51KDX5PdnyQjxERsZKjVZiLJvn4/hBsyik8/7L1IIjSVNt7yzXGtNPlm7O94oof/Lf++T3uWlTpF/a0qsizTEoNJxB7fqsZRpsa/aHXK9S4+j6np+OTjFus7ngh5UrC/Pe49j09MDRtpWcYRUtZD7LxwX7DiZ4BwjOHn0UXvGYFjAHRKX6CVx93W4jZvRNR6CpxjyTK5qK3dFxVdNLniv2G1vO+LiWWKIxmf+Ve7qYoMuoHjAXRKb+BNJEzZ7dFTtm/mSLPMRoe7q6Wv8LgO6bfdNlub54VMpXaXfHbLuY7Pfnp9eh4d+gN0CgAAvQM6BQCA3gGdAgBA74BOAQCgd0CnAADQO6BTAADoHdAp4JSAsLmx93Ho7elZ8HHkWaZ4N6Pt6E8PH/4Yzra8eW9Ap3JHZGCQgRp4iCTjU/cc/FrzZvdxW+XqKjEqbIdcDcU1Az9p8HAoFI4+yzTvZrT2rL/M0uAKyRem7QfQqdw5mE5pNtKlgm1zy1o9Myy7OP0ibdJ0YGiblrVzyInJB8QuxnYrkN0Mq6DQlmWNNs2aqyvNu5nsmrR+HBBbp+4T0KncOZhO5dbhouz+zQXtzsg0DjkxeYC2ca/A0ZhliiMQFc3VlebdjBZ9DqPc/GdfgE5pQWlxy5X98tjQFQve73rufIlVuIXUQAV0I136xmYlUndd3ixpe9JZU9L2IDjp+bCK4a7+/oPZDVqEXG2zXfnL58TYt6j65g/YnhinhjtHIJszMiVCWmLoJN+MfdpEtk+/c63Zim2fxOIoagZKm+BMWxL/JLP1WuJURQPNuRsM4l6D0hlZJTFU7+ZMOTDm+oEArg+0xFA8o7nTlBbPxK6L94xO7yGn3lBKhKpoyDLdphm9/rGfz6/l8p1nr7hrqPg+q5U/V5IK72YU8bZainoCksrBLs//RvbLawB0SgtKi1u0FRwqt97wr7Ds0o/91cWkWlMD1ZA9oFSMjNWQd2GwOZSxsqqy/s8jkWmvo6Rk8PkGJcLV0b4Kg6G2f3SFPOLSBrta+lPqKdTSn9p6Plh+Hl+XjQX66wxNnij3F0UaaDbBVJ3CjqO2+qHxOEos+D60lg8Ft9TqPEqM9p8Rm3xSXJVVdIri3Yw2xu+U2W4MxxK7bMTX2YKlmZYYmmc01VWZOM+03o+wiI3cbzVdck5xzVl5Q4NblAjV0JJlgrI/xa7ElrlakXjpvSaaCpBV12zlr/Ru5jNuaHDPpdO8M++9WmJgLgwGxR0vVUCntKC0uMXVKPV8SN9CaqAKciNdFRc6NeTSwHvm8Xv6k/Ho1ER0iRYhtp0RqoskBvHV1VBPoQadwnbAwjM2rQg0aaDaBNN0ivQXUo0Bex9nmWQhvxK77VBclak6RfNuxqmqqHBOisOoiaF5RtNclSU1IT0NpLyh8R1KhGpoyTJGbtNMeklD2ErIUllVwqRjkFfXrOWP75esPijTE/W1lxos7R6un6gB0CkNUCxuxT5T6WpNDVRB3rYlxlX4n+KbqkQhDThAUuGoEeLAM32juHJJYpCcrIJ6ChWJUSI6P60IImlA856mElxc+DxhgEEgNsEZndqOetr4n8hPzDbkVDQS7oJyZ2RaYsSBGSh3ViUxSs9oLnJuzCh1VZaUXqaQcbDkhnLQIqSjJcsYfBXJaYv+rgpb18PZ+MYr3wdF6X9JEolRyTIB3y/Z7SDpkXs87wPQqb0R3ct0Ncq5PyW/8Tn2p8SJ5KFGKNIaSQxadCqf/Sn8QCbuw1Sb4IxOcX2ZMv4nOELey1QDOIGy0Q1G7IxMSwz9RlDefGVNjNgzOoXYVZnen1Le0DS0CBVoyDJGfhXRrZT8SxTOkyXLNO9mMhcmV/x9ADq1NzSL25znpxRGuipGxioo6o28wvHTSfII1XRKizOyegoViaGAJ3pE81O4ypKhDXbU3Ix47EbDGSJPNJtgLBwXnaF1PH1jNJC5rd3drQlnbWmLC886o/j80yeT2eZrVoY7JO+bKK7KtMTs0ryb2aXhLmv9J8+WErvsq7HPbn8WfIuoiaF4RtMtnqecjVaHVzo/pbyh1AjV2TvLGLlNM15iUkLWOm3OulsZMh1GzpNXV/Xyp3g3c6NET7NVMljmx33M+Zsj5NXEXoBOaYBqcZt5tXf53UtWoUZRA+lQjHS1ve8j7K1T1AjVdIpTDw3OyGop1KJTmfd9trrzJcKjdTPkbi0hF/3rFzeEhNFsguNht52U6jXXF91nhQwk4+GHvfy7M8bW2Ouby7K+lH9ZjoWYP6Q5I9MSQ6Ymh2TezZk3kplyoCSG5hlNd1WmvO+j3VBahOpoyTKH3KY5GZ/6ErtaG4qqOv6lr6Wy2cPPfSuqq1r5U72bZ90XGPykEY45+Hl07qfZv7ybAnQKOGJEI6yjg3gKG9u9C5rGiQXBsWSZ5t2MFv3dFZb2f8akuso9kO7ZS/b4HFEK0CngiDkWndolm0gsZf3Pjm4TybFz1FnmeuVK7+ZEbPiGjfJeb8FnL6/ucI29FvUY1QGdAgBA74BO5RXuoUEG8BKyvEEXk1zyXcNzIBLEn/PNG0s+u+LCWl8q5/JbAFABdAoAAL0DOgUAgN4BnQIAQO+ATgEAoHdOvk7hVfpnBIsSAAAKkYLoTyVG+yz8FgQAAAqQgtApvMVAueUSAIACAXQKAAC9UxA6haeormq0+AAA4MRREDq1uxYcbLQ1OcfjWRybAAA4qRSGTsWnnJdBpwCgUIFxHwAAegfm0QEA0DugUwAA6J2C0ClY5wkABc3J1ynYNwMAhU5B9KcAAChoQKcAANA7oFMAAOgd0CkAAPQO6BQAAHoHdOpEQj7hz2O1+8L44/08zDXfEqzPAAqNgtAp/Klos7VnZE04JqDVKfc18nHt4mb3dIHtqVF+1BsT87YYYL0rUIAURn9qfXb43tfji+IVVCjiaTbZPZFNdunXF8uF1nRBp4BTRcGO++gtuVAAnQJOFSddp9IfDTa3eKOpsOBgieibvFk+xouWx4auWAwGxtZy7YqNnBj1tpQKUWWaPWJjPw62lnORMrZr7qlVtMvGvHZDizeGzxP9ZDcRGxlqtZkMBpPNcW8qnq+pItAp4FRRGP0psVIIaOhPoa3gUHnZ7dG1JLv0Y3+1tckzj6g6hSLeNlv90HgcJRZ8H1rLh4JbdJ1CC942U+NQ8C1iI77OivLB51v4BAWyj5tjgZR9pX2P77ODTgGnitOsU5thV2NRT2Ad/52OgaJTaM7dYLjsnkvgwPVAT1GjKxyn6VRizn3Z0OCew/NkaD1ws6jWFc6Pcx/oFHCqOM06FR8fqEqdQtEpFPU0kWaPoxK6OTznBoOrGZ1C856mEvITHKFwCo9apwj6UwCwH6A/pdafIh0i0ux3wq5aQ5vUL1Q07sM9LH52DEdoaPJE8+/cADoFnCpO9fzUxvidssz8VDGJYSXQU1njnNxmZzytVgMvT1sTztrSFtdkHO2i+PzTJ5NvEFGxmruh7c2Ix240nCFzW2hr6m6t6arrlzW0m4xHfnoysZwnyQKdAk4Vp1mnOKVKv+87d77EynejNsP3Wi2MgalwuO7eOFs9MB7f5UQn/LC3npMtfGpjr29uG+1uhtytJeS93l+/uJEaP6LV8MOP6814AMeUNvf6ZvK0vhR0CjhVFIZO5Q5F6fQM6BRwqgCd4jmBOoV7dxzS/X2gU0AhAjoFAIDeAZ0CAEDvgE4BAKB3QKcAANA7oFMAAOgd0CkAAPQO6BQAAHoHdOqAaFrvfoKR7am22n0LODg56awwSlzYM25f6b3T4g3eApLiSq1HFa0CS9t1kY2TGVLXBU43oFMaee3vKq91TadtWk6sTu2s+T8y7W04Q9EaDmxxYyqvKq/sCawIQRjZKtn96BQJRPFJV8vZCudkUmJACAACoFMakS9YP7E6pVEIqDqFNa6o9dOvblSKJTt3nRKlCnQKoAA6tTeij1BxCCMR3MZMF6+9V8ONeZjq/pElvgkqfYe5Nmy78pfP+/E25qLqmz8s0S0U0Hbobo2x/cH0z55OLk6Trf3B7DZ3qjLCBZ/ddt5ux7udmfO9j38jF07GQ99013GXYMx11++HVhE1ws3nYkdmxu5bItemQdWptfGBcxXO4O++a8ZWbyyTkdx1ClsMGjuGV7LoFLs8/1s8/5Y5gB4BndIIrT9lMJb1P1tDL30OW4P7V64J0XyHV0f7KgyG2v7RFbTocxhTvqBKdqZdtdbKqqr6/ieRea/DdG4wuK4Wocn+rzPx5HbY1WDq8C2yvPNMq2eG3cU+M6bau1NbiBqhqOeSHZpOoV/dDRVd/tfJkLOi6GZgPS0aueoUio8P1Ve2+6JS43kRO/PeqyUG5sJgUPLxM+CUADqlDdzmy7kmKhwKOsWLTrpZUn2H8X+FQPybKmIUQ2Ul0FNmKP7T6Aa2uYpOTEXjG2oRCk0+JRzYyc/0kX+NDMXW/F2mRld4kxYh1x3DZn6mLv9ezZ0kWyA1mb0y3MHrLL5EgzO0Qc7kyEGnhEsUVfd+H2NxGWGdEhBNoqOor73UYGn3zKYvCpwiQKe0oZAYHHCmfzTBNa10sxS3bQJ+AYYDz/SN4k7UHjqFz5QqiGqEqSYvCIREBTJXUUbIIf55FpSnocRo/xkhHRy8NSBPjv2pRX93hbX78QqODuanAAqgU9pQSIyo4aWbJdV3WNRoFZFIUTbvvSLcoz9F0QuVQCX0xKRUbyPkbBAp4N46hef4UkNF/NKQrGxIlSHCA1jjH1xhrq8EOgVQAJ3SBl43ZOsc/h3Ff/78zrcLLKLpFNV3WNRo961TqhHS5qcqHN45yfwUTS8EiekcXkFvg5//7dGCymQZ5bd4/j7VhyKLGzKfmthbp3bXRnqs5Z2+CMtdd6jReME9uyPSepTuUqnoFD/uY87fHPldItrA6QB0SiOJ2ONb1fjdXoXDPRFH1P4U15yUvsOi/+5bp1QjNFadP7fH+z4OWoT4m6nf91YX7fUlVMVv8ecq+Jl4DPkYD5mrUlnnSUJ4+Gmm9ItLxlx/2x9LjYNTBUe6VK3uWfLBsQypOPl5dO442ztKoGABnTpxUNWn8EHx6Xv2kuK+ZzCRfgoBnTpxnE6d4kad5dUdrrHXp02fAQzoFAAAegd0CgAAvQM6BQCA3gGdAgBA74BOAQCgd0CnCprM4ibVlUciN4hcTenQ8ov/HZFZGrDL4amI6iotANAE6FRBo1xZuvmLt7vBzMmWrf3u2KJIU2Rryg/AVsRjr3VObguHBDTnab7sDAmrQ08oKD7hdlRgvTdfdePNPbu7Oy/cF8zWnpG9tnPnxM6s+wJT0RNYFo7zQmLWfYWxfhzgN13plULVKbn9piqZ3XCFiFynNsKuy1aHN8Im18Y+qSm+4V9J93Ry1qntSWdtm3tWvBEHe2DVki0y+0Gj4yiH5rucE5z+XjW13o+wiaXpmWVB2tdnh+99PS4Weo1ozx33UJkZdj0YFy8ZO/zqijZnv3d9PfZ6/zk5SgpVpzS3utTefeGwwJDpFHanSVkGy51qctQptBUcqmzzLkiqezw42NzmjeyzCWjfipx7H1ALh7uwNreN1oVdXdU56TqF2NhjYpXJ9ckbur2/cA8aqv0mVz9eB/5chzfKMZamwWfYfnM9OHhOOIsj/W0C9uXI4Ds2fKKwlS9H0JufhprO4v1077RfsZ7HW+QytU1ca5XWneL/itqk9hTKdAo/jdNGfSuBnire3o9Aa/P7sNB8O9p3weF7KTl541lfSSfeJq0G+5u//xKxoLLWdXvDm0g8ocaRmlNT3ju1u6wsw5yQXoXfbJhc8l0jSTRniovrS9aUtD0ITno+rGK4MfX7D2Y3NOeOyoLPTmp1RpU0VldOVWtKG/+Ai8ty4d0r5YyhqHrgJ/qe0iWfnU9ORje5aqDFfpYj+Wbs0yYLl9uKd641W3HRaK6uWut/hpOuU3j3f1G3fxUl43NPvwumeuWUVofY5VfLLNpl570OW2awIH9AbS942031nwbjSXbB12mtHwxSb3G6svKkt+Aq4foUDcT2M7EUuF1taPNEt6n3iWbdSb3xGlNIkOmUJLOyboKixPZlobk20lNyXTCWEdhZC3xcktWQj/iCXvevJlF85ofvJt4IN0+cax6Ve6dIM60MaaTbJ4/6zSNQ+1OyS2PTG2NlVWX9n0ci015HCRflltbcqSOvnFqq65uopw23iJ1f3Q2V3f6lnTl3Q7beoCw9mu1nt54Plp/Hp7GxQH8dcR/SXF211v8MJ12ncCkYS9udT2Ykz32F/SZR608d1RaDubyq1JS+cRLnJg7s6GRN9TK4HofsgwX7RzzUSt8eyn2ieoHSbvy+UpiLTu3DQjO5MtxVKtshjH4f7rzUN/pWOKSBq6ax3O58Mid5eCodR+n3TnGXqWXI/ysXtOgUuZyhiuQ3GY9OTUTjO1pzp45clbRU18mXXjtJLZfCOnzruUgUqRch0ymcWS32szglgqFYOgbN1VVr/ce/4Tn581NoNXT/ZmOpibFdc0/xfia0In7zuMtS3fUwHN+O+jpL0zcOnyi+i/hYeMoS1DpKmvtT4pSkb0/mPm1zTz9yn0j9EINjFN14NO9pKsE3XmsKCbJykI/7xBqn0CntoIi3rXFgfFU4JGANqrgzviHcEBWS8dA/ehptjGQAq9AFlXsnzx29DIX/STj8/hRJi1hBMNpylwWFTuGr7FFdx6I565QW+1lRSmg6lb26aq3/5EqEQplHZxefu96z8l7g+FBexKJilVQUUTgBP6KtIkfdnNmjP8WJRRm5T1TrTtGNx/ZPZEJkXymUlYO4A5LyAiX/4Di4TqGIp7n2bgh/HScNbY2CKoml5186rNWpzpe8JavdO8VdppZh7sjTQ6DplOIkwh65y8aeOkWpDLjOkFMOrlPC6fLilbBHfyp7ddVa/zOc+Hn0rdC/f+nnBn2IDbvqjR8M8y/aFfabuC3xzrZs2N1kJlNa5MSQs8L04fDKVjzovvMowu6uTzkvmVq++oXrq6N45Glg4k22R60GVscH6kTzU+T24Lt40RlaZyP3W40Gcnuo1p2kA1zDSQB26TQKluT7SaG8qu1nXYJWC02uhl254H4hGWBxtfOcbI0Cja1p35dkWMROu+rLOoZfkVC546javVPeZVoZ5s5BdUpb7kigCgqd0lBdE0ejU7sb/zlQJpqfwjForq5a63+Gk65TyTdjf8czFxyp930Euf0mNzyccr2Lz2NqOj75uMX6jidC5unY3x73nscnpoaNKD79sPci6auaShtv+eZy9WXLvO+rqy0Rql087LZzieEu6vqi+yxfLyjWnZwIhNytJeQNyF+/uCFUoH2kUFnVMus83xl69kpUFRQ6pdFCc+v5YMW7QmEK4DUKFc2ePdcjoDf/cddRSzKSeiNGguWOo2r3jnaXKWWYKwfUKa25y4JCpzRU17Uj0qnM+z5b3fkSIQat1VVz/U9RKOO+E4Gy2uWbPaqaGIVOkRq2l4Um2hi/U+HwSdc7cl3IJvkaBaBgkSldXgCdOkKOR6fwgww/o/be3ydaEITRYqG5HOi5gEdewiEBr1HoEgbgQOEDOgXoG7Qy3FmaenchQFujAAC5AToF5AC7FJr4TbrZDLGvwxPRk73xGNAboFMAAOgd0CkAAPQO6BQAAHoHdArQJ4iNjT/yPg69Par3htvR0Yf/9iSc2noF6AnQqcOnoO0fabnLB9shV0NxzcBPa0cmG2hltL+WaXCFJRuAAF0AOnXoHK79434sHI/C/pGau0OH2GDYbgVy3bS0P/AGnfwLPXAAQKcOHeo2ixzIZXXo4a8sPezcUcHdT2uN1m3Mhwd66XMUazVdAY6Qk65T6yHnRWPbN9OTns6qIgNT0f7g1208tfHjYGt5ZhsUXpZta2w5bzYwlkttV2wmA1M/MPZmF3vCXCdGkda67m9C8eRuYmzg7Flhz8fGs77i2oFxrtIqIuQvruCQ7R/VLBwpHIX9Iy131PJPxkPfdNdx6WHMddfvh9TLXwU0524wiLuBSutIsQSL10MrPSFF+4EyP1G7oVxUDkawAUiD2NeR37JvxAPyzInvT2F/CWNZVWVT/8jMvPeaiavEbMTbZqsfGo+jxILvQ2v5UDAx52mqErwNscXixpz7snlgbANv0bZ7Ipu77IyntbTWObGF/XOryBMVb6Yt541isL+SNMItNaXi0LJtFbtYaLN/3E+fSHmmPCR3+0d57ijlvzHhrC1t9cywu3jfvKn27hS9/NUughKj/WfEXk4U60i6TtE8IWk6pX5DsSGBQWJiuRN5cNXCMDVDwT28tIA8cvLHfdgjguH3yqJ4dGIi+l+z3NM4VdXwf7knc1i6dxzXbPNAIJRxVkx/BQRtjP6pGDcSrqdwQYgWP95lEWaZ8dGiU9rtHxUWjllQ6JT8t4dg/6jInaL818QXFSbIqOWvdhGiO6J/06yOqDpF9YSk6FS2G4rPkezcRrF/tluMljbPLMyvHx8nX6fwmKJUbDEs2nvLc24w+H9o7cQ/Kmpy+Ff8Aa64VX3/63+l/e1oEWbZF6JFp0iccvWh2j+KErYnmaYrIP+tPCcHsH9U5A7HqSj/9Bn4gGv2oVx0ShQhVaeye0JmSh5FPU3kJ/JiEN9QHG32+wscA4WhU5IHIH788p9LyCBrhHw7ofanuL9f+7tsxY2X0p0CWoRZ0KxT9JYqs3/McqaCTNMVkP/2EOwfqTqlKP+c+lMkSlFG9upPZfeETJc834fFP8lyQ8kcnKQMAT1QgDq1u4XnR1pck1yXBMXnnz6ZfIOoOkXmp6wdXsn8FAfRLIMh896HEiH/DyoH1Sm6/aPSwlEdhU7lwf5xb50ixVXh8M5l5qe2IvvSqd2V4Q6jqI9GsY7ku71aPCHx8Ba/OsS32Grg5Un1huJVF0yFMyQaeZNxH8NU94+Q73EBx0Ih6hRXR8MPe/mP+jG2xl7f3LZKO1G+7+PB7eRsWiloEWYRqgPqlIr9I3eq3MJRFYVO5cH+UYNO4QGs9H2fPPK9dIpfH4DNefljmnWkVk9ItBm+10reFTpcd2+crSZJVbmhtPUQ/Dw6d1NSnwgEjoGTr1NAAZJc8d+wGtu9C7KhmWjcd/iQi1re98VkF03GZ/7VbuJfegDHA+gUoEvwvhkL+f6FEEDIn05xY8pvu222Ns8L+azVks9uOdfx2U+vs3VlgfwCOgUAgN4BnToAso+M8vDrs/NNet25mMN+jy77DCfPkWQPAKiATgEAoHdApwAA0DugUwAA6B3QKQAA9A7oFAAAeievOoUtkGDDAQAAOZLv/tTqaF+DaAMKAADAvsm3TsXHB+rE+9oAAAD2C+gUAAB6J986tR4cvCA1PAIAANgf+dYptBEcqrW1/m1c1bQfAAAgO3nXqc2pzxpApwAAyAEY9wEAoHdgHh0AAL0DOgUAgN7Jt07BOk8AAHIlrzoF+2YAADgE8t2fAgAAyBXQKQAA9A7oFAAAeofXKZhIAgBAv6T7U/BiDgAAnZLWKVjoBACATgGdAgBA76R1CjbiAQCgU9I6BQYsAADolIxOgQELAAD6BMZ9AADoHZhHBwBA74BOAQCgd9I6Bes8AQDQKbxOwb4ZAAD0S7o/BQAAoFNApwAA0DugUwAA6B3QKQAA9A7oFAAAegd0CgAAvQM6BQCA3gGdAgBA3+zu/v9J4Gb1DzNiEgAAAABJRU5ErkJggg=="
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "## Simple Hill Climbing",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Simple hill climbing is the simplest way to implement a hill-climbing algorithm. It only evaluates the neighbor’s node state at a time and selects the first one which optimizes current cost and set it as a current state. It only checks it’s one successor state, and if it finds better than the current state, then move else be in the same state.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "- <b>Step 1:</b> Evaluate the initial state, if it is goal state then return success and Stop.\n- <b>Step 2:</b> Loop Until a solution is found or there is no new operator left to apply.\n- <b>Step 3:</b> Select and apply an operator to the current state.\n- <b>Step 4:</b> Check new state:\n  - <b>i.</b> If it is goal state, then return success and quit.\n  - <b>ii.</b> else if it is better than the current state then assigns new state as a current state.\n  - <b>iii.</b> else if not better than the current state, then return to step 2.\n- <b>Step 5:</b> Exit.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Steepest-Ascent hill climbing",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The steepest-Ascent algorithm is a variation of the simple hill-climbing algorithm. This algorithm examines all the neighboring nodes of the current state and selects one neighbor node which is closest to the goal state. This algorithm consumes more time as it searches for multiple neighbors.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "- <b>Step 1:</b> Evaluate the initial state, if it is goal state then return success and stop, else make the current state as your initial state.\n- <b>Step 2:</b> Loop until a solution is found or the current state does not change.\n   - <b>i.</b> Let S be a state such that any successor of the current state will be better than it.\n   - <b>ii.</b> For each operator that applies to the current state;\n        - Apply the new operator and generate a new state.\n        - Evaluate the new state.\n        - If it is goal state, then return it and quit, else compare it to the S.\n        - If it is better than S, then set new state as S.\n        - If the S is better than the current state, then set the current state to S.\n- <b>Step 3:</b> Exit.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Stochastic hill climbing",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Stochastic hill climbing does not examine for all its neighbors before moving. Rather, this search algorithm selects one neighbor node at random and evaluate it as a current state or examine another state.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Task:",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![tree.png](attachment:tree.png)",
      "metadata": {},
      "attachments": {
        "tree.png": {
          "image/png": 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"
        }
      }
    },
    {
      "cell_type": "code",
      "source": "#Implement Simple HCS\nMyGraph = {\n    'A' : ['B','C'],\n    'B' : ['A','C','D'],\n    'C' : ['A','B','E'],\n    'D' : ['B','E','F'],\n    'E' : ['C','D','F'],\n    'F' : ['D','E'],\n}\nHeuristicVales={\n    'A' : 25,\n    'B' : 8,\n    'C' : 20,\n    'D' : 6,\n    'E' : 12,\n    'F' : 0,\n}\nStartNode='A'\nGoalNode='F'\n\ndef HCS_simple():\n    Node=StartNode\n    print('Traversing::')\n    while True:\n        print(Node)\n        if Node==GoalNode:\n            print('Goal Node Found!')\n            break\n        minHeurNode=Node\n        for n in MyGraph[Node]:\n            if HeuristicVales[minHeurNode] > HeuristicVales[n]:\n                minHeurNode=n\n        if minHeurNode==Node:\n            print('Maxima Goal point '+Node+' is Found!')\n            break\n        Node=minHeurNode\nHCS_simple()\n            ",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Traversing::\n",
            "A\n",
            "B\n",
            "D\n",
            "F\n",
            "Goal Node Found!\n"
          ]
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "code",
      "source": "#Implement Steepest-Ascent HCS\nMyGraph = {\n    'A' : ['B','C'],\n    'B' : ['A','C','D'],\n    'C' : ['A','B','E'],\n    'D' : ['B','E','F'],\n    'E' : ['C','D','F'],\n    'F' : ['D','E'],\n}\nHeuristicValues={\n    'A' : 25,\n    'B' : 8,\n    'C' : 20,\n    'D' : 6,\n    'E' : 12,\n    'F' : 0,\n}\nStartNode='A'\nGoalNode='F'\n\ndef HCS_Steepest_Ascent():\n    Node=StartNode\n    Queue=[Node]\n    Visited=[]\n    print('Traversing::')\n    while Queue:\n        Node=Queue.pop()\n        print(Node)\n        if Node==GoalNode:\n            print('Goal Node Found!')\n            break\n            \n        childList=[]\n        for x in reversed(MyGraph[Node]):\n            if x not in Visited and HeuristicValues[Node]>HeuristicValues[x]:\n                childList.append(x)\n        Queue.extend(childList)\n        Visited.append(Node)\n        \nHCS_Steepest_Ascent()",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Traversing::\n",
            "A\n",
            "B\n",
            "D\n",
            "F\n",
            "Goal Node Found!\n"
          ]
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "code",
      "source": "#Implement Stochastic HCS\nimport random\nMyGraph = {\n    'A' : ['B','C'],\n    'B' : ['A','C','D'],\n    'C' : ['A','B','E'],\n    'D' : ['B','E','F'],\n    'E' : ['C','D','F'],\n    'F' : ['D','E'],\n}\nHeuristicValues={\n    'A' : 25,\n    'B' : 8,\n    'C' : 20,\n    'D' : 6,\n    'E' : 12,\n    'F' : 0,\n}\nStartNode='A'\nGoalNode='F'\n\ndef HCS_stochastic():\n    Node=StartNode\n    print('Traversing::')\n    while True:\n        print(Node)\n        if Node==GoalNode:\n            print('Goal Node Found!')\n            break\n        randChildNode=MyGraph[Node][random.randint(0,len(MyGraph[Node])-1)]\n        if randChildNode==Node:\n            print('Maxima Goal point '+Node+' is Found!')\n            break\n        Node=randChildNode\nHCS_stochastic()",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Traversing::\n",
            "A\n",
            "B\n",
            "D\n",
            "E\n",
            "D\n",
            "B\n",
            "A\n",
            "B\n",
            "C\n",
            "B\n",
            "D\n",
            "E\n",
            "D\n",
            "F\n",
            "Goal Node Found!\n"
          ]
        }
      ],
      "execution_count": 9
    }
  ]
}