Chen’s autonomous driving models and mechanical arm installations collectively reveal the potential of multi-agent systems, particularly embodied intelligence, to generate complex behaviors from simple foundational rules. These works showcase how such systems offer rich expressive possibilities for art. Beyond technological demonstration, they embody Chen’s profound understanding of social, cultural, technological, and natural dynamics. In such works, the artist transcends the traditional role of creator, instead assuming the position of a designer or director. By setting the rules of the system, then observing, interpreting, or intervening in its outcomes, Chen transforms each agent into a narrative unit. These agents act within their defined rules, gradually weaving intricate behaviors and storylines.This approach not only demonstrates the artistic potential of multi-agent systems but also highlights the centrality of narrative in art. Chen functions less as a storyteller and more as an observer and interpreter, crafting the framework for a “narrative system” and extracting stories from its unfolding. These works serve as an open narrative experiment, emphasizing the boundless possibilities of narrative dynamics in artistic creation.
The Decision-Making Process in Multi-Agent Systems
In multi-agent systems (MAS), agents use various strategy models to make decisions, allowing them to update their beliefs about other players based on their observations and interpretations of the game state. These beliefs are continuously adjusted as new evidence is introduced, enabling agents to reassess the situation and respond effectively in environments with incomplete information.The decision-making process of an agent involves several key steps. First, through belief modeling, the agent forms views on the potential strategies and behaviors of other players. Next, the agent selects a discussion strategy, often developed through reinforcement learning, to determine its actions during the game's discussion phase, such as presenting evidence, accusing others, or defending itself. Finally, after gathering sufficient information, the agent makes a final decision based on its current beliefs and discussion strategy, potentially altering its game tactics or responding to the actions of other players.This process demonstrates how agents in decentralized environments rely on internal models and interactions with others to coordinate their actions and decisions. Agents aim to optimize their strategies by influencing the beliefs and expectations of others, showcasing autonomy and social capability in complex interactive settings. These mechanisms of interaction and coordination illustrate MAS's unique advantages in addressing dynamic, incomplete information scenarios.
In multi-agent systems (MAS), agents use various strategy models to make decisions, allowing them to update their beliefs about other players based on their observations and interpretations of the game state. These beliefs are continuously adjusted as new evidence is introduced, enabling agents to reassess the situation and respond effectively in environments with incomplete information.The decision-making process of an agent involves several key steps. First, through belief modeling, the agent forms views on the potential strategies and behaviors of other players. Next, the agent selects a discussion strategy, often developed through reinforcement learning, to determine its actions during the game's discussion phase, such as presenting evidence, accusing others, or defending itself. Finally, after gathering sufficient information, the agent makes a final decision based on its current beliefs and discussion strategy, potentially altering its game tactics or responding to the actions of other players.This process demonstrates how agents in decentralized environments rely on internal models and interactions with others to coordinate their actions and decisions. Agents aim to optimize their strategies by influencing the beliefs and expectations of others, showcasing autonomy and social capability in complex interactive settings. These mechanisms of interaction and coordination illustrate MAS's unique advantages in addressing dynamic, incomplete information scenarios.
Dynamic Narrative Construction with MAS
Dynamic narrative construction introduces mechanisms of change and evolution into storytelling, allowing narratives to unfold based on participant interactions or environmental changes. Unlike static, pre-scripted stories, dynamic narratives are open-ended systems that adapt to user behavior and decisions, creating nonlinear and multi-outcome pathways. Traditional AI-driven interactivity typically relies on preset scripts and rules, making AI behavior predictable and focused on responding to user input within developer-defined boundaries. While such interactions can display intelligence, they remain heavily constrained.In MAS-driven narratives, the dynamic evolution of beliefs and decision-making processes among agents forms the core of transforming static stories into dynamic ones. Initially, each agent develops preliminary beliefs based on its role and the initial information available. As the interaction progresses, agents observe the actions of others and environmental changes, updating their beliefs with new information. This belief adjustment is achieved through algorithms that integrate new data with existing beliefs, enabling agents to form a more accurate understanding of the current scenario.Beliefs directly influence agents' decisions. For example, if an agent perceives another as an opponent, it may adopt defensive or offensive strategies. These decisions, in turn, impact the game state and the beliefs of other agents, creating a feedback loop where beliefs and decisions continually interact. This iterative process drives narrative progression, transforming a static framework into a dynamic system full of variability and possibilities.
Dynamic narrative construction introduces mechanisms of change and evolution into storytelling, allowing narratives to unfold based on participant interactions or environmental changes. Unlike static, pre-scripted stories, dynamic narratives are open-ended systems that adapt to user behavior and decisions, creating nonlinear and multi-outcome pathways. Traditional AI-driven interactivity typically relies on preset scripts and rules, making AI behavior predictable and focused on responding to user input within developer-defined boundaries. While such interactions can display intelligence, they remain heavily constrained.In MAS-driven narratives, the dynamic evolution of beliefs and decision-making processes among agents forms the core of transforming static stories into dynamic ones. Initially, each agent develops preliminary beliefs based on its role and the initial information available. As the interaction progresses, agents observe the actions of others and environmental changes, updating their beliefs with new information. This belief adjustment is achieved through algorithms that integrate new data with existing beliefs, enabling agents to form a more accurate understanding of the current scenario.Beliefs directly influence agents' decisions. For example, if an agent perceives another as an opponent, it may adopt defensive or offensive strategies. These decisions, in turn, impact the game state and the beliefs of other agents, creating a feedback loop where beliefs and decisions continually interact. This iterative process drives narrative progression, transforming a static framework into a dynamic system full of variability and possibilities.
Key Features of MAS-Driven Narratives
MAS-driven narratives differ significantly from traditional dynamic storytelling. First, they exhibit high autonomy and decentralization, as agents independently drive the narrative based on their judgments and interactions rather than relying on centralized scripts. Second, agents can respond in real-time to environmental changes and other agents' actions, demonstrating adaptability and immediacy beyond traditional narratives. Third, the complexity and unpredictability of MAS narratives are significantly greater, as each agent’s independent decision-making introduces countless variables, diverging from the fixed paths of conventional storytelling. Lastly, MAS narratives enhance interactivity and immersion, allowing users to influence agent behavior directly and alter the story’s outcomes, creating a deeply engaging experience.
MAS-driven narratives differ significantly from traditional dynamic storytelling. First, they exhibit high autonomy and decentralization, as agents independently drive the narrative based on their judgments and interactions rather than relying on centralized scripts. Second, agents can respond in real-time to environmental changes and other agents' actions, demonstrating adaptability and immediacy beyond traditional narratives. Third, the complexity and unpredictability of MAS narratives are significantly greater, as each agent’s independent decision-making introduces countless variables, diverging from the fixed paths of conventional storytelling. Lastly, MAS narratives enhance interactivity and immersion, allowing users to influence agent behavior directly and alter the story’s outcomes, creating a deeply engaging experience.
MAS Simulations and Narrative Synergy
The interplay between MAS simulations and narrative construction lies in their ability to simulate and explore complex social, cultural, and human behavioral dynamics. Narrative frameworks provide contextual backgrounds and plot structures, while MAS introduces dynamism and interactivity, bringing stories to life with predictive and experimental capacities. For example, in urban planning or social policy simulations, narrative constructs offer historical, cultural, and economic contexts. MAS then populates these contexts with agents simulating various social roles—citizens, policymakers, business leaders—interacting based on their strategies and goals. These interactions form a dynamic, nonlinear process capable of producing diverse outcomes.MAS simulations also demonstrate complex causality, conflict resolution, and cooperation dynamics, enriching narrative layers and helping observers understand otherwise elusive social mechanisms and changes. For instance, agents might simulate different social groups' behavior in resource allocation conflicts or explore individual responses to policy changes within specific cultural contexts.
The interplay between MAS simulations and narrative construction lies in their ability to simulate and explore complex social, cultural, and human behavioral dynamics. Narrative frameworks provide contextual backgrounds and plot structures, while MAS introduces dynamism and interactivity, bringing stories to life with predictive and experimental capacities. For example, in urban planning or social policy simulations, narrative constructs offer historical, cultural, and economic contexts. MAS then populates these contexts with agents simulating various social roles—citizens, policymakers, business leaders—interacting based on their strategies and goals. These interactions form a dynamic, nonlinear process capable of producing diverse outcomes.MAS simulations also demonstrate complex causality, conflict resolution, and cooperation dynamics, enriching narrative layers and helping observers understand otherwise elusive social mechanisms and changes. For instance, agents might simulate different social groups' behavior in resource allocation conflicts or explore individual responses to policy changes within specific cultural contexts.
Predictive and Experimental Potential of MAS
A critical function of MAS simulations is their capacity for experimentation and prediction. Once narrative parameters and initial conditions are set, simulations can explore the impact of various decisions and strategies on story development. This capability is invaluable to storytellers, policymakers, and researchers, enabling them to anticipate and evaluate potential outcomes and trajectories. By combining narrative construction and MAS simulation, Chen Baoyang and other practitioners not only deepen our understanding of complex social phenomena but also enhance the scientific rigor and precision of decision-making processes.
A critical function of MAS simulations is their capacity for experimentation and prediction. Once narrative parameters and initial conditions are set, simulations can explore the impact of various decisions and strategies on story development. This capability is invaluable to storytellers, policymakers, and researchers, enabling them to anticipate and evaluate potential outcomes and trajectories. By combining narrative construction and MAS simulation, Chen Baoyang and other practitioners not only deepen our understanding of complex social phenomena but also enhance the scientific rigor and precision of decision-making processes.