Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems (1709.08071v2)

Abstract: Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.

• The paper provides a comprehensive survey of seven agent modelling methodologies, detailing their assumptions, strengths, and limitations.
• It evaluates techniques such as policy reconstruction, type-based reasoning, and recursive reasoning with practical applications in dynamic environments.
• It identifies nine critical open research problems that pave the way for future advancements in autonomous agent interactions.

Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

The paper, "Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems," by Stefano V. Albrecht and Peter Stone, documents an extensive review of methodologies related to the modelling of autonomous agents. This intricate area in artificial intelligence focuses on creating agents capable of effectively interacting and reasoning about other agents. Thus, the paper provides a categorical survey of the salient agent modelling methods and pinpoints significant open problems, potentially guiding the trajectory of future research in this domain.

The authors categorize agent modelling methods into seven major types: policy reconstruction, type-based reasoning, classification, plan recognition, recursive reasoning, graphical models, and group modelling. Each method is critically evaluated based on its assumptions about agents and environments, possible strengths, and limitations.

Policy Reconstruction is the task of constructing models that predict the actions and policies of agents. These models rely on conditioning action distributions on historical interaction data. Variations within this method consider deterministic or stochastic actions and how these methods can adapt to dynamically changing behaviors of the modelled agents.

Type-Based Reasoning provides a method to quickly adapt to interacting with agents by utilizing a predefined set of agent types. Beliefs are updated about these types based upon observed actions, demonstrating utility in rapidly converging towards applicable strategies in non-stationary environments.

Classification methods are employed when the properties of interest extend beyond actions to include behavior categories or intents. Machine learning approaches, incorporating classification and regression, can predict outcomes such as player strategy categories or trust levels, thus facilitating improved decision-making processes.

Plan Recognition, unlike previous methodologies, focuses on inferring goals and plans from observed actions. This often involves hierarchical plan libraries and is particularly useful in applications such as intrusion detection and adaptive user interfaces.

Recursive Reasoning assumes the nesting of beliefs within beliefs. This approach models the nested reasoning process to predict actions, benefiting domains requiring higher-order strategic reasoning, although complexity increases with the depth of recursion.

Graphical Models implement structured representations like Influence Diagrams (IDs) to depict dependencies within multiagent systems. This methodology utilizes the explicit structure of these models for a compact representation, operational efficiency, and allows for detailed mental models of other agents' decision-making processes.

Group Modelling addresses interactions with correlated or strategizing entities by assessing the group as a unit, capturing inter-agent cooperation and deriving more sophisticated interaction strategies.

The paper closes by identifying nine critical open problems in the field:

1. The synergetic integration of multiple modelling methods.
2. Challenges faced in policy reconstruction under conditions of partial observability.
3. The need for efficient and safer exploration strategies in model learning.
4. The systematic discovery of decision factors influencing agents' behaviors.
5. Necessity for efficient computational implementations suited for complex environments.
6. Adapting to non-stationary behaviors, prevalent in dynamic areas such as human-agent interactions.
7. Addressing real-world action duration and impacts within modelling.
8. Approaches tailored for the open multiagent systems where agents frequently enter and exit.
9. Enabling autonomous contemplation about models' correctness and the revision of modelling processes.

Overall, this survey reveals a robust cross-section of research fields contributing to agent modeling in AI and points out the need for innovative solutions to push the boundaries of agent autonomy and efficacy in varied domains. By compiling comprehensive methodologies and marking future research directions, the authors present the paper as a cornerstone for researchers exploring autonomous agent interactions under a wide variety of circumstances.