Merge pull request #55 from sotopia-lab/neubig/papers1

Added a few papers

bibtex_to_table.py

@@ -224,20 +224,20 @@ def area_plot_analysis(df: pd.DataFrame) -> str:
     return format_json_data
 
 
-def preprocess_entry(entry: dict, taxonomy:dict[str, list[str]]) -> None:
+def preprocess_entry(entry: dict, taxonomy: dict[str, list[str]]) -> None:
     entry["ID"] = entry.get("ID", "")
 
     if not entry.get("title", ""):
-        raise ValueError("Title field is missing")
+        raise ValueError(f"Title field is missing for {entry}")
 
     if not entry.get("author", ""):
-        raise ValueError("Author field is missing")
+        raise ValueError(f"Author field is missing for {entry}")
 
     if not entry.get("year", ""):
-        raise ValueError("Year field is missing")
+        raise ValueError(f"Year field is missing for {entry}")
 
     if not entry.get("url", ""):
-        raise ValueError("URL field is missing")
+        raise ValueError(f"URL field is missing for {entry}")
 
     authors_list = entry["author"].split(" and ")
     authors_processed = []
@@ -261,7 +261,7 @@ def preprocess_entry(entry: dict, taxonomy:dict[str, list[str]]) -> None:
         # entry["month"] = month_name.capitalize()
         pass
     else:
-        raise ValueError(f"Month field is missing for paper {entry['title']}")
+        raise ValueError(f"Month field is missing for paper {entry}")
 
     # get the venue of the paper
     if "journal" in entry:

components/data/chartData.tsx

@@ -1,27 +1,27 @@
 export const bar_data = [
    {
   name: 'Text',
-  overall: 157,
+  overall: 158,
   collaboration: 21,
   competition: 9,
   mixed_objectives: 51,
-  text: 157,
+  text: 158,
   embodied: 7,
   virtual: 24,
   robotics: 5,
   two_agents: 55,
-  reinforcement_learning: 20,
+  reinforcement_learning: 21,
   agents_with_personas: 23,
   human: 88,
   not_applicable: 142,
-  rule_based: 51,
+  rule_based: 52,
   prompting_and_in_context_learning: 46,
   qualitative: 35,
-  human_agent: 54,
+  human_agent: 55,
   implicit_objectives: 18,
   more_than_three_agents: 29,
   finetuning: 35,
-  agents_with_memory: 15,
+  agents_with_memory: 16,
   more_omniscient: 3,
   pretraining: 18,
   model_based: 44,
@@ -71,7 +71,7 @@ export const bar_data = [
   prompting_and_in_context_learning: 16,
   more_than_three_agents: 10,
   rule_based: 48,
-  not_applicable: 35,
+  not_applicable: 39,
   finetuning: 17,
   qualitative: 10,
   human_agent: 21,
@@ -132,29 +132,29 @@ export const area_data = [
   human: 1,
   rule_based: 1,
   human_agent: 1,
-  agent_teams: 0,
-  prompting_and_in_context_learning: 0,
-  more_omniscient: 0,
-  fully_omniscient: 0,
-  education: 0,
-  more_than_three_agents: 0,
-  agents_with_personas: 0,
-  text: 0,
   more_information_asymmetrical: 0,
   finetuning: 0,
-  agents_with_memory: 0,
-  implicit_objectives: 0,
-  health: 0,
-  simulated_humans: 0,
-  virtual: 0,
-  competition: 0,
+  model_based: 0,
   pretraining: 0,
+  more_than_three_agents: 0,
+  more_omniscient: 0,
+  text: 0,
   two_agents: 0,
-  policy: 0,
-  embodied: 0,
+  education: 0,
+  simulated_humans: 0,
   not_applicable: 0,
+  health: 0,
+  agent_teams: 0,
+  implicit_objectives: 0,
+  virtual: 0,
+  embodied: 0,
+  policy: 0,
+  prompting_and_in_context_learning: 0,
   qualitative: 0,
-  model_based: 0,
+  agents_with_personas: 0,
+  fully_omniscient: 0,
+  competition: 0,
+  agents_with_memory: 0,
 },
 {
   name: '2016',
@@ -176,51 +176,51 @@ export const area_data = [
   more_than_three_agents: 1,
   model_based: 1,
   education: 1,
+  more_information_asymmetrical: 0,
+  pretraining: 0,
+  more_omniscient: 0,
+  simulated_humans: 0,
+  not_applicable: 0,
+  health: 0,
   agent_teams: 0,
+  implicit_objectives: 0,
+  policy: 0,
   prompting_and_in_context_learning: 0,
-  more_omniscient: 0,
   fully_omniscient: 0,
-  more_information_asymmetrical: 0,
   agents_with_memory: 0,
-  implicit_objectives: 0,
-  health: 0,
-  simulated_humans: 0,
-  pretraining: 0,
-  policy: 0,
-  not_applicable: 0,
 },
 {
   name: '2017',
-  text: 1,
+  text: 2,
   mixed_objectives: 1,
-  reinforcement_learning: 3,
+  reinforcement_learning: 4,
   two_agents: 2,
-  agents_with_memory: 1,
-  rule_based: 4,
-  human_agent: 2,
+  agents_with_memory: 2,
+  rule_based: 5,
+  human_agent: 3,
   virtual: 3,
   not_applicable: 4,
   competition: 1,
   robotics: 1,
   qualitative: 1,
   human: 1,
-  collaboration: 0,
-  agent_teams: 0,
-  prompting_and_in_context_learning: 0,
-  more_omniscient: 0,
-  fully_omniscient: 0,
-  education: 0,
-  more_than_three_agents: 0,
-  agents_with_personas: 0,
   more_information_asymmetrical: 0,
   finetuning: 0,
-  implicit_objectives: 0,
-  health: 0,
-  simulated_humans: 0,
+  model_based: 0,
   pretraining: 0,
-  policy: 0,
+  more_than_three_agents: 0,
+  more_omniscient: 0,
+  education: 0,
+  simulated_humans: 0,
+  health: 0,
+  agent_teams: 0,
+  implicit_objectives: 0,
+  collaboration: 0,
   embodied: 0,
-  model_based: 0,
+  policy: 0,
+  prompting_and_in_context_learning: 0,
+  agents_with_personas: 0,
+  fully_omniscient: 0,
 },
 {
   name: '2018',
@@ -250,9 +250,9 @@ export const area_data = [
   model_based: 1,
   education: 1,
   more_omniscient: 1,
-  agent_teams: 0,
   more_information_asymmetrical: 0,
   pretraining: 0,
+  agent_teams: 0,
   policy: 0,
 },
 {
@@ -279,14 +279,14 @@ export const area_data = [
   agent_teams: 1,
   model_based: 1,
   health: 1,
+  more_information_asymmetrical: 0,
+  pretraining: 0,
   more_omniscient: 0,
-  fully_omniscient: 0,
   education: 0,
-  more_information_asymmetrical: 0,
-  agents_with_memory: 0,
   simulated_humans: 0,
-  pretraining: 0,
   policy: 0,
+  fully_omniscient: 0,
+  agents_with_memory: 0,
 },
 {
   name: '2020',
@@ -317,8 +317,8 @@ export const area_data = [
   policy: 1,
   education: 1,
   more_omniscient: 1,
-  agent_teams: 0,
   more_information_asymmetrical: 0,
+  agent_teams: 0,
   agents_with_memory: 0,
 },
 {
@@ -349,10 +349,10 @@ export const area_data = [
   more_omniscient: 1,
   agents_with_personas: 1,
   prompting_and_in_context_learning: 1,
-  fully_omniscient: 0,
   more_information_asymmetrical: 0,
-  agents_with_memory: 0,
   pretraining: 0,
+  fully_omniscient: 0,
+  agents_with_memory: 0,
 },
 {
   name: '2022',
@@ -384,8 +384,8 @@ export const area_data = [
   education: 6,
   policy: 2,
   more_omniscient: 2,
-  fully_omniscient: 0,
   more_information_asymmetrical: 0,
+  fully_omniscient: 0,
 },
 {
   name: '2023',

components/papers.tsx

@@ -30,6 +30,18 @@ export const data: Paper[] = [
 },
 
 
+{ title: "Persuasive technology: using computers to change what we think and do",
+    date: "12/2002",
+    environments: "n/a",
+    agents: "n/a",
+    evaluation: "n/a",
+    other: "n/a",
+    url: "https://dl.acm.org/doi/10.1145/764008.763957",
+    bibtex: "@article{fogg2002persuasive,\n  title={Persuasive technology: using computers to change what we think and do},\n  author={Fogg, Brian J},\n  journal={Ubiquity},\n  volume={2002},\n  number={December},\n  pages={2},\n  year={2002},\n  month={12},\n  publisher={ACM New York, NY, USA},\n  url={https://dl.acm.org/doi/10.1145/764008.763957},\n  environments = {n/a},\n  agents = {n/a},\n  evaluation = {n/a},\n  other = {n/a},\n}",
+    subsection: "",
+},
+
+
 { title: "Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions",
     date: "04/2024",
     environments: "collaboration, competition, mixed_objectives, text, embodied, virtual, robotics",
@@ -438,6 +450,18 @@ export const data: Paper[] = [
 },
 
 
+{ title: "AI wolf contest\u2014development of game AI using collective intelligence\u2014",
+    date: "04/2017",
+    environments: "text",
+    agents: "reinforcement_learning, agents_with_memory",
+    evaluation: "rule_based",
+    other: "human_agent",
+    url: "https://link.springer.com/chapter/10.1007/978-3-319-57969-6_8",
+    bibtex: "@inproceedings{toriumi2017ai,\n  title={AI wolf contest\u2014development of game AI using collective intelligence\u2014},\n  author={Toriumi, Fujio and Osawa, Hirotaka and Inaba, Michimasa and Katagami, Daisuke and Shinoda, Kosuke and Matsubara, Hitoshi},\n  booktitle={Computer Games: 5th Workshop on Computer Games, CGW 2016, and 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, Held in Conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, New York, USA, July 9-10, 2016, Revised Selected Papers 5},\n  pages={101--115},\n  year={2017},\n  month={4},\n  organization={Springer},\n  url={https://link.springer.com/chapter/10.1007/978-3-319-57969-6_8},\n  agents = {reinforcement_learning, agents_with_memory},\n  environments = {text},\n  evaluation = {rule_based},\n  other = {human_agent},\n}",
+    subsection: "environments/language",
+},
+
+
 { title: "SEAN: Social Environment for Autonomous Navigation",
     date: "09/2020",
     environments: "mixed_objectives, embodied",

docs/helper.md

@@ -6,6 +6,8 @@
 
 [07, 2023] [Communicative Agents for Software Development](https://arxiv.org/abs/2307.07924), Chen Qian et al., arXiv
 
+[12, 2002] [Persuasive technology: using computers to change what we think and do](https://dl.acm.org/doi/10.1145/764008.763957), Brian J Fogg et al., Ubiquity
+
 ### environments/language
 
 [3, 2024] [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://arxiv.org/abs/2403.04132), Wei-Lin Chiang et al., arXiv preprint arXiv:2403.04132
@@ -62,6 +64,8 @@
 
 [9, 2017] [Deal or No Deal? End-to-End Learning of Negotiation Dialogues](https://aclanthology.org/D17-1259), Lewis et al., Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
 
+[4, 2017] [AI wolf contest—development of game AI using collective intelligence—](https://link.springer.com/chapter/10.1007/978-3-319-57969-6_8), Fujio Toriumi et al., Computer Games: 5th Workshop on Computer Games, CGW 2016, and 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, Held in Conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, New York, USA, July 9-10, 2016, Revised Selected Papers 5
+
 [8, 2016] [A persona-based neural conversation model](https://aclanthology.org/P16-1094/), Jiwei Li et al., arXiv preprint arXiv:1603.06155
 
 [11, 2009] [The anatomy of ALICE](https://link.springer.com/chapter/10.1007/978-1-4020-6710-5_13), Richard S Wallace et al., n/a