day_19_arxiv_artificial_life_nlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d18c815",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# for the explode statement; convert string to list\n",
    "from ast import literal_eval\n",
    "\n",
    "from IPython.display import SVG \n",
    "from sknetwork.visualization import svg_graph \n",
    "from sknetwork.data import Bunch \n",
    "from sknetwork.ranking import PageRank "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e8c36c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_graph(G, show_names=False, node_size=1, font_size=10, edge_width=0.5): \n",
    "\n",
    "    adjacency = nx.to_scipy_sparse_matrix(G, nodelist=None, dtype=None, weight='weight', format='csr') \n",
    "\n",
    "    names = np.array(list(G.nodes())) \n",
    "\n",
    "    graph = Bunch() \n",
    "    graph.adjacency = adjacency \n",
    "    graph.names = np.array(names) \n",
    "\n",
    "    pagerank = PageRank() \n",
    "\n",
    "    scores = pagerank.fit_transform(adjacency) \n",
    "\n",
    "    if show_names: \n",
    "        \n",
    "        image = svg_graph(graph.adjacency, font_size=font_size, node_size=node_size, names=graph.names, width=700, height=500, scores=scores, edge_width=edge_width) \n",
    "\n",
    "    else: \n",
    "\n",
    "        image = svg_graph(graph.adjacency, node_size=node_size, width=700, height=500, scores = scores, edge_width=edge_width) \n",
    "\n",
    "    return SVG(image) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5e7e69f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_published</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>summary</th>\n",
       "      <th>url</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2023-10-18 14:50:39+00:00</td>\n",
       "      <td>Emergence and criticality in spatiotemporal sy...</td>\n",
       "      <td>[Alessandro Scirè]</td>\n",
       "      <td>This work concerns the long-term dynamics of a...</td>\n",
       "      <td>http://arxiv.org/pdf/2310.12018v1</td>\n",
       "      <td>nlin.AO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2023-10-13 11:58:18+00:00</td>\n",
       "      <td>Role of Morphogenetic Competency on Evolution</td>\n",
       "      <td>[Lakshwin Shreesha]</td>\n",
       "      <td>The relationship between intelligence and evol...</td>\n",
       "      <td>http://arxiv.org/pdf/2310.09318v1</td>\n",
       "      <td>cs.NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>2023-06-22 19:32:53+00:00</td>\n",
       "      <td>Amorphous Fortress: Observing Emergent Behavio...</td>\n",
       "      <td>[M Charity, Dipika Rajesh, Sam Earle, Julian T...</td>\n",
       "      <td>We introduce a system called Amorphous Fortres...</td>\n",
       "      <td>http://arxiv.org/pdf/2306.13169v1</td>\n",
       "      <td>cs.AI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>2023-05-16 12:01:08+00:00</td>\n",
       "      <td>Capturing Emerging Complexity in Lenia</td>\n",
       "      <td>[Sanyam Jain, Aarati Shrestha, Stefano Nichele]</td>\n",
       "      <td>This research project investigates Lenia, an a...</td>\n",
       "      <td>http://arxiv.org/pdf/2305.09378v5</td>\n",
       "      <td>cs.NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>2023-04-24 02:36:25+00:00</td>\n",
       "      <td>Coevolution of Camouflage</td>\n",
       "      <td>[Craig Reynolds]</td>\n",
       "      <td>Camouflage in nature seems to arise from compe...</td>\n",
       "      <td>http://arxiv.org/pdf/2304.11793v2</td>\n",
       "      <td>cs.GR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                date_published  \\\n",
       "117  2023-10-18 14:50:39+00:00   \n",
       "146  2023-10-13 11:58:18+00:00   \n",
       "830  2023-06-22 19:32:53+00:00   \n",
       "1079 2023-05-16 12:01:08+00:00   \n",
       "1220 2023-04-24 02:36:25+00:00   \n",
       "\n",
       "                                                  title  \\\n",
       "117   Emergence and criticality in spatiotemporal sy...   \n",
       "146       Role of Morphogenetic Competency on Evolution   \n",
       "830   Amorphous Fortress: Observing Emergent Behavio...   \n",
       "1079             Capturing Emerging Complexity in Lenia   \n",
       "1220                          Coevolution of Camouflage   \n",
       "\n",
       "                                                authors  \\\n",
       "117                                  [Alessandro Scirè]   \n",
       "146                                 [Lakshwin Shreesha]   \n",
       "830   [M Charity, Dipika Rajesh, Sam Earle, Julian T...   \n",
       "1079    [Sanyam Jain, Aarati Shrestha, Stefano Nichele]   \n",
       "1220                                   [Craig Reynolds]   \n",
       "\n",
       "                                                summary  \\\n",
       "117   This work concerns the long-term dynamics of a...   \n",
       "146   The relationship between intelligence and evol...   \n",
       "830   We introduce a system called Amorphous Fortres...   \n",
       "1079  This research project investigates Lenia, an a...   \n",
       "1220  Camouflage in nature seems to arise from compe...   \n",
       "\n",
       "                                    url category  \n",
       "117   http://arxiv.org/pdf/2310.12018v1  nlin.AO  \n",
       "146   http://arxiv.org/pdf/2310.09318v1    cs.NE  \n",
       "830   http://arxiv.org/pdf/2306.13169v1    cs.AI  \n",
       "1079  http://arxiv.org/pdf/2305.09378v5    cs.NE  \n",
       "1220  http://arxiv.org/pdf/2304.11793v2    cs.GR  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = r'data/arxiv_artificial_life.csv'\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "\n",
    "df['date_published'] = pd.to_datetime(df['date_published'])\n",
    "df['authors'] = df['authors'].apply(literal_eval)\n",
    "\n",
    "# remove everything that doesn't mention artificial life explicitely in the summary; too aggressive, but useful for now\n",
    "mask = df['summary'].str.contains('artificial life', case=False)\n",
    "df = df[mask]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e398fe0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132, 6)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261d438e",
   "metadata": {},
   "source": [
    "# Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "40823f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date_published</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>summary</th>\n",
       "      <th>url</th>\n",
       "      <th>category</th>\n",
       "      <th>date_ym</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2023-10-18 14:50:39+00:00</td>\n",
       "      <td>Emergence and criticality in spatiotemporal sy...</td>\n",
       "      <td>[Alessandro Scirè]</td>\n",
       "      <td>This work concerns the long-term dynamics of a...</td>\n",
       "      <td>http://arxiv.org/pdf/2310.12018v1</td>\n",
       "      <td>nlin.AO</td>\n",
       "      <td>2023-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2023-10-13 11:58:18+00:00</td>\n",
       "      <td>Role of Morphogenetic Competency on Evolution</td>\n",
       "      <td>[Lakshwin Shreesha]</td>\n",
       "      <td>The relationship between intelligence and evol...</td>\n",
       "      <td>http://arxiv.org/pdf/2310.09318v1</td>\n",
       "      <td>cs.NE</td>\n",
       "      <td>2023-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>2023-06-22 19:32:53+00:00</td>\n",
       "      <td>Amorphous Fortress: Observing Emergent Behavio...</td>\n",
       "      <td>[M Charity, Dipika Rajesh, Sam Earle, Julian T...</td>\n",
       "      <td>We introduce a system called Amorphous Fortres...</td>\n",
       "      <td>http://arxiv.org/pdf/2306.13169v1</td>\n",
       "      <td>cs.AI</td>\n",
       "      <td>2023-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>2023-05-16 12:01:08+00:00</td>\n",
       "      <td>Capturing Emerging Complexity in Lenia</td>\n",
       "      <td>[Sanyam Jain, Aarati Shrestha, Stefano Nichele]</td>\n",
       "      <td>This research project investigates Lenia, an a...</td>\n",
       "      <td>http://arxiv.org/pdf/2305.09378v5</td>\n",
       "      <td>cs.NE</td>\n",
       "      <td>2023-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>2023-04-24 02:36:25+00:00</td>\n",
       "      <td>Coevolution of Camouflage</td>\n",
       "      <td>[Craig Reynolds]</td>\n",
       "      <td>Camouflage in nature seems to arise from compe...</td>\n",
       "      <td>http://arxiv.org/pdf/2304.11793v2</td>\n",
       "      <td>cs.GR</td>\n",
       "      <td>2023-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                date_published  \\\n",
       "117  2023-10-18 14:50:39+00:00   \n",
       "146  2023-10-13 11:58:18+00:00   \n",
       "830  2023-06-22 19:32:53+00:00   \n",
       "1079 2023-05-16 12:01:08+00:00   \n",
       "1220 2023-04-24 02:36:25+00:00   \n",
       "\n",
       "                                                  title  \\\n",
       "117   Emergence and criticality in spatiotemporal sy...   \n",
       "146       Role of Morphogenetic Competency on Evolution   \n",
       "830   Amorphous Fortress: Observing Emergent Behavio...   \n",
       "1079             Capturing Emerging Complexity in Lenia   \n",
       "1220                          Coevolution of Camouflage   \n",
       "\n",
       "                                                authors  \\\n",
       "117                                  [Alessandro Scirè]   \n",
       "146                                 [Lakshwin Shreesha]   \n",
       "830   [M Charity, Dipika Rajesh, Sam Earle, Julian T...   \n",
       "1079    [Sanyam Jain, Aarati Shrestha, Stefano Nichele]   \n",
       "1220                                   [Craig Reynolds]   \n",
       "\n",
       "                                                summary  \\\n",
       "117   This work concerns the long-term dynamics of a...   \n",
       "146   The relationship between intelligence and evol...   \n",
       "830   We introduce a system called Amorphous Fortres...   \n",
       "1079  This research project investigates Lenia, an a...   \n",
       "1220  Camouflage in nature seems to arise from compe...   \n",
       "\n",
       "                                    url category  date_ym  \n",
       "117   http://arxiv.org/pdf/2310.12018v1  nlin.AO  2023-10  \n",
       "146   http://arxiv.org/pdf/2310.09318v1    cs.NE  2023-10  \n",
       "830   http://arxiv.org/pdf/2306.13169v1    cs.AI  2023-06  \n",
       "1079  http://arxiv.org/pdf/2305.09378v5    cs.NE  2023-05  \n",
       "1220  http://arxiv.org/pdf/2304.11793v2    cs.GR  2023-04  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date_ym'] = df['date_published'].dt.strftime('%Y-%m')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dbe9cad5",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>paper_count</th>\n",
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       "    <tr>\n",
       "      <th>date_ym</th>\n",
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       "    <tr>\n",
       "      <th>1993-06</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1994-05</th>\n",
       "      <td>1</td>\n",
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       "      <th>1994-12</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1997-02</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "         paper_count\n",
       "date_ym             \n",
       "1993-06            1\n",
       "1994-05            1\n",
       "1994-12            1\n",
       "1996-05            1\n",
       "1997-02            1"
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     "execution_count": 58,
     "metadata": {},
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   ],
   "source": [
    "agg_df = df[['date_ym', 'title']].groupby('date_ym').count()\n",
    "\n",
    "agg_df.columns = ['paper_count']\n",
    "\n",
    "agg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "40c57349",
   "metadata": {},
   "outputs": [
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       "      <th>2023-02</th>\n",
       "      <td>4</td>\n",
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       "      <th>2023-04</th>\n",
       "      <td>2</td>\n",
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       "      <th>2023-05</th>\n",
       "      <td>1</td>\n",
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       "      <th>2023-06</th>\n",
       "      <td>1</td>\n",
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       "      <th>2023-10</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "         paper_count\n",
       "date_ym             \n",
       "2023-02            4\n",
       "2023-04            2\n",
       "2023-05            1\n",
       "2023-06            1\n",
       "2023-10            2"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f81962c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_= agg_df.plot(figsize=(20,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ce273cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['category'].value_counts()[0:20].plot.barh(figsize=(12,8)).invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "85870668",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_category_titles(df):\n",
    "    \n",
    "    df = df.copy()\n",
    "    \n",
    "    categories = list(df['category'].value_counts().index)\n",
    "\n",
    "    for category in categories:\n",
    "\n",
    "        print('Category: {}'.format(category))\n",
    "        print()\n",
    "\n",
    "        titles = sorted(df[df['category']==category]['title'])\n",
    "        \n",
    "        for title in titles:\n",
    "            print(title)\n",
    "\n",
    "        print()\n",
    "        print('-------------------')\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "093e563e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category: cs.NE\n",
      "\n",
      "A Trillion Genetic Programming Instructions per Second\n",
      "Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarm Using Embodied Evolution\n",
      "An Artificial Life Simulation Library Based on Genetic Algorithm, 3-Character Genetic Code and Biological Hierarchy\n",
      "An Artificial Neural Network Functionalized by Evolution\n",
      "An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue\n",
      "An artifcial life approach to studying niche differentiation in soundscape ecology\n",
      "Artificial Life in Game Mods for Intuitive Evolution Education\n",
      "Artificial Life using a Book and Bookmarker\n",
      "Artificial life: sustainable self-replicating systems\n",
      "Aspects of Evolutionary Design by Computers\n",
      "Biologically Inspired Design Principles for Scalable, Robust, Adaptive, Decentralized Search and Automated Response (RADAR)\n",
      "Capturing Emerging Complexity in Lenia\n",
      "Challenges and Opportunities of Evolutionary Robotics\n",
      "Co-generation of game levels and game-playing agents\n",
      "Complex Networks\n",
      "DXNN Platform: The Shedding of Biological Inefficiencies\n",
      "Design of a P System based Artificial Graph Chemistry\n",
      "Digital Genesis: Computers, Evolution and Artificial Life\n",
      "Evolutionary Generation of Visual Motion Illusions\n",
      "Evolving Differentiable Gene Regulatory Networks\n",
      "Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization\n",
      "Increased Complexity and Fitness of Artificial Cells that Reproduce Using Spatially Distributed Asynchronous Parallel Processes\n",
      "Increasing Behavioral Complexity for Evolved Virtual Creatures with the ESP Method\n",
      "JohnnyVon: Self-Replicating Automata in Continuous Two-Dimensional Space\n",
      "Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity\n",
      "Modelling SARS-CoV-2 coevolution with genetic algorithms\n",
      "On the Liveliness of Artificial Life\n",
      "Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity\n",
      "Passive and Driven Trends in the Evolution of Complexity\n",
      "Qualities, challenges and future of genetic algorithms: a literature review\n",
      "Role of Morphogenetic Competency on Evolution\n",
      "Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients\n",
      "Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm\n",
      "Self-Replicating Machines in Continuous Space with Virtual Physics\n",
      "SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications\n",
      "Sooner than Expected: Hitting the Wall of Complexity in Evolution\n",
      "The Importance of Open-Endedness (for the Sake of Open-Endedness)\n",
      "The Self-Organizing Symbiotic Agent\n",
      "The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities\n",
      "Towards Large-Scale Simulations of Open-Ended Evolution in Continuous Cellular Automata\n",
      "Towards a Framework for Observing Artificial Evolutionary Systems\n",
      "WebAL-1: Workshop on Artificial Life and the Web 2014 Proceedings\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.AI\n",
      "\n",
      "AIR5: Five Pillars of Artificial Intelligence Research\n",
      "Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs\n",
      "An Inductive Formalization of Self Reproduction in Dynamical Hierarchies\n",
      "Artificial life properties of directed interaction combinators vs. chemlambda\n",
      "Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity\n",
      "Chemlambda, universality and self-multiplication\n",
      "Emotional Responses in Artificial Agent-Based Systems: Reflexivity and Adaptation in Artificial Life\n",
      "Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research\n",
      "From Alife Agents to a Kingdom of N Queens\n",
      "Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems\n",
      "Intelligence as information processing: brains, swarms, and computers\n",
      "Machine Love\n",
      "Motility at the origin of life: Its characterization and a model\n",
      "Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines\n",
      "Perspective: Purposeful Failure in Artificial Life and Artificial Intelligence\n",
      "Perspectives for Strong Artificial Life\n",
      "Quantifying Morphological Computation based on an Information Decomposition of the Sensorimotor Loop\n",
      "Quantifying Natural and Artificial Intelligence in Robots and Natural Systems with an Algorithmic Behavioural Test\n",
      "The Agent-based Modelling for Human Behaviour Special Issue\n",
      "Thoughts on an Unified Framework for Artificial Chemistries\n",
      "Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems\n",
      "Transfer Dynamics in Emergent Evolutionary Curricula\n",
      "Turing's Imitation Game has been Improved\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: nlin.AO\n",
      "\n",
      "Diversity Evolution\n",
      "Emergence and criticality in spatiotemporal synchronization: the complementarity model\n",
      "Emergence of Self-Reproducing Metabolisms as Recursive Algorithms in an Artificial Chemistry\n",
      "Evolution of Complexity: Introduction to the Workshop\n",
      "Introduction to Random Boolean Networks\n",
      "Network Complexity of Foodwebs\n",
      "Open-Ended Artificial Evolution\n",
      "Possible Laws for Artificial Life Evolution\n",
      "Provenance of Lyfe: Chemical Autonomous Agents Surviving through Associative Learning\n",
      "Scaling up the self-optimization model by means of on-the-fly computation of weights\n",
      "Self-Organization and Artificial Life\n",
      "Self-Organization and Artificial Life: A Review\n",
      "Some Techniques for the Measurement of Complexity in Tierra\n",
      "The differences between natural and artificial life. Towards a definition of life\n",
      "The influence of parsimony and randomness on complexity growth in Tierra\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: quant-ph\n",
      "\n",
      "Artificial Life in Quantum Technologies\n",
      "Design and Simulation of an Autonomous Quantum Flying Robot Vehicle: An IBM Quantum Experience\n",
      "QKSA: Quantum Knowledge Seeking Agent\n",
      "Quantum Artificial Life in an IBM Quantum Computer\n",
      "Quantum machine learning and quantum biomimetics: A perspective\n",
      "Replication and Evolution of Quantum Species\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: adap-org\n",
      "\n",
      "Comment on \"Self-organized criticality in living systems\" by C. Adami\n",
      "Evolutionary Learning in the 2D Artificial Life System \"Avida\"\n",
      "Preferential Partner Selection in an Evolutionary Study of Prisoner's Dilemma\n",
      "Propagation of Information in Populations of Self-Replicating Code\n",
      "Swarms, Phase Transitions, and Collective Intelligence\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: physics.gen-ph\n",
      "\n",
      "Emergence in artificial life\n",
      "Evolution in the Multiverse\n",
      "Postcorrection and mathematical model of life in Extended Everett's Concept\n",
      "The Evolution and Development of the Universe\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: nlin.CG\n",
      "\n",
      "Classification of Complex Systems Based on Transients\n",
      "Evolving Structures in Complex Systems\n",
      "Lenia - Biology of Artificial Life\n",
      "Lenia and Expanded Universe\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: q-bio.PE\n",
      "\n",
      "Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems\n",
      "Evolution of Complexity\n",
      "Plasticity and evolvability under environmental variability: the joint role of fitness-based selection and niche-limited competition\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.RO\n",
      "\n",
      "Decentralized, Self-organizing, Potential field-based Control for Individuallymotivated, Mobile Agents in a Cluttered Environment: A Vector-Harmonic Potential Field Approach\n",
      "Shape Change and Control of Pressure-based Soft Agents\n",
      "Sustainably Grown: The Underdog Robots of the Future\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.IT\n",
      "\n",
      "JIDT: An information-theoretic toolkit for studying the dynamics of complex systems\n",
      "Mechanical generation of networks with surplus complexity\n",
      "Towards a Synergy-based Approach to Measuring Information Modification\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.MA\n",
      "\n",
      "Melting Pot 2.0\n",
      "Self-Regulated Artificial Ant Colonies on Digital Image Habitats\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: q-bio.OT\n",
      "\n",
      "A number theoretical observation about the degeneracy of the genetic code\n",
      "Foundations of biology\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.CE\n",
      "\n",
      "Proceedings Second International Workshop on Hybrid Systems and Biology\n",
      "Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.IR\n",
      "\n",
      "Centralized reward system gives rise to fast and efficient work sharing for intelligent Internet agents lacking direct communication\n",
      "mARC: Memory by Association and Reinforcement of Contexts\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.OH\n",
      "\n",
      "On the Origin of Samples: Attribution of Output to a Particular Algorithm\n",
      "Organized Complexity: is Big History a Big Computation?\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cond-mat.dis-nn\n",
      "\n",
      "Critical and Near-Critical Branching Processes\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: math.NA\n",
      "\n",
      "In search of an evolutionary coding style\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: physics.bio-ph\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ab Initio Modeling of Ecosystems with Artificial Life\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: physics.optics\n",
      "\n",
      "The Enlightened Game of Life\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.SE\n",
      "\n",
      "Arduino Tool: For Interactive Artwork Installations\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.CV\n",
      "\n",
      "Brain Intelligence: Go Beyond Artificial Intelligence\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.DC\n",
      "\n",
      "Artificial life, complex systems and cloud computing: a short review\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: physics.app-ph\n",
      "\n",
      "Robotics: Science preceding science fiction\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.GR\n",
      "\n",
      "Coevolution of Camouflage\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.CR\n",
      "\n",
      "Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: q-bio.NC\n",
      "\n",
      "On Artificial Life and Emergent Computation in Physical Substrates\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: q-bio.BM\n",
      "\n",
      "Building blocks of protein structures -- Physics meets Biology\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: math.CT\n",
      "\n",
      "Rosen's no-go theorem for regular categories\n",
      "\n",
      "-------------------\n",
      "\n",
      "Category: cs.CY\n",
      "\n",
      "Cities as they could be: Artificial Life and Urban Systems\n",
      "\n",
      "-------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# you can read about the categories here: https://arxiv.org/category_taxonomy\n",
    "# cs.NE Computer Science: (Neural and Evolutionary Computing)\n",
    "# cs.AI Computer Science: (Artificial Intelligence)\n",
    "# nlin.AO Nonlinear Sciences: (Adaptation and Self-Organizing Systems)\n",
    "\n",
    "# Artificial Life is approached from different sciences\n",
    "# But nlin.AO could be cool to zoom in on and explore\n",
    "\n",
    "show_category_titles(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "06208cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://arxiv.org/pdf/1609.07111v2\n"
     ]
    }
   ],
   "source": [
    "search_title = 'Organized Complexity: is Big History a Big Computation?'\n",
    "\n",
    "url = df[df['title']==search_title]['url'].values[0]\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcbe6ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Today's Goal will be to find the signal that I want; Artificial Life, not Artificial Intelligence"
   ]
  }
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