~

NIPS2020/main.tex

@@ -154,20 +154,20 @@ intelligence in computer science.
 
 %Recent effort for emergence is not correct
 Recent efforts in computer science start to explore the emergence of symbolic
-language but among multiple agents in a virtual environment by leveraging the
+language but in a virtual environment among multiple agents by leveraging the
 neural network based methods, i.e., deep reinforcement learning. In these works,
-researchers put multiple agents into a situation that agents have to communicate
-with each other for achieving a pre-defined goal cooperatively. Researchers hope
+researchers put multiple agents into a scenario that agents have to achieve a
+pre-defined goal cooperatively. Researchers hope
 that agents can evolve a stable communication protocol (e.g., symbolic language)
-among themselves by the compulsory cooperation through communication. Those
+among themselves by the compulsory communication for cooperation. Such
 works can be roughly classified into two categories, referential game and
 multi-agent reinforcement learning (MARL), based on the environment setting.
 However, previous works, no matter referential game related or multi-agent reinforcement
-learning related, ignore the independence of agents in training or
-inference. Those agents usually share one or more of the model parameters, loss functions,
+learning related, ignore the independence of agents in \note{training or
+inference.} Agents usually share one or more of the model parameters, loss functions,
 observation of environments, and thusly can be taken as one huge brain with
 multiple connected sensors (agents). In other words, previous works did not
-really achieve emergence of symbolic language among \emph{multiple agents}. 
+really achieve the emergence of symbolic language among \emph{multiple} agents. 
 %difference from group intelligence, population intelligence, community intelligence
 
 Besides, previous works also failed on the \emph{naturally emergence}. In
@@ -176,7 +176,8 @@ listening, respectively. In MARL, agents xxxxx\rmk{how}
 
 %In this paper, we proposed a SIC model
 In this paper, To achieve the naturally emergence of symbolic language among
-individual agents, we propose a novel three-step model---the Self-grounding-Introspection-Cooperation
+individual agents, we propose a novel three-step model---the
+Self-grounding-Introspection-Cooperation 
 (SIC) model. In the first step of SIC model, i.e., \emph{Self-grounding}, each agent is trained
 in the virtual environment to perform tasks successfully, through a
 self-playing process. In the second step of SIC model, i.e., \emph{Introspection}, each
@@ -187,9 +188,8 @@ environment and are required to perform a certain task together, where the
 symbolic language appears. With the SIC model, we could achieve goal of
 naturally emergence of symbolic language among multiple agents.  Experimental
 results show that \rmk{xxxx} To the best of our knowledge, we are the first to
-\rmk{xxx} This work presents a trial to
-shed some lights on the origin of human language and the emergence of
-intelligence in individual human.
+\rmk{xxx} This work presents a trial to shed some lights on the origin of human
+language and the emergence of intelligence in individual human.
 
 %Our contribution
 We made the following contributions.