EVA 2.0: Emotional and rational multimodal argumentation between virtual agents

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Niklas Rach ◽  
Klaus Weber ◽  
Yuchi Yang ◽  
Stefan Ultes ◽  
Elisabeth André ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
User Feedback ◽  
Virtual Agents ◽  
Multi Agent System ◽  
Dialogue Game ◽  
Multi Agent ◽  
The Individual ◽  
Minimal Bias ◽  
Intuitive Interface ◽  
Emotional Level

Abstract Persuasive argumentation depends on multiple aspects, which include not only the content of the individual arguments, but also the way they are presented. The presentation of arguments is crucial – in particular in the context of dialogical argumentation. However, the effects of different discussion styles on the listener are hard to isolate in human dialogues. In order to demonstrate and investigate various styles of argumentation, we propose a multi-agent system in which different aspects of persuasion can be modelled and investigated separately. Our system utilizes argument structures extracted from text-based reviews for which a minimal bias of the user can be assumed. The persuasive dialogue is modelled as a dialogue game for argumentation that was motivated by the objective to enable both natural and flexible interactions between the agents. In order to support a comparison of factual against affective persuasion approaches, we implemented two fundamentally different strategies for both agents: The logical policy utilizes deep Reinforcement Learning in a multi-agent setup to optimize the strategy with respect to the game formalism and the available argument. In contrast, the emotional policy selects the next move in compliance with an agent emotion that is adapted to user feedback to persuade on an emotional level. The resulting interaction is presented to the user via virtual avatars and can be rated through an intuitive interface.

Combat Robot Strategy Adaptation Using Multiple Learning Agents

2012 ◽  
Author(s):  
Thomas Recchia ◽  
Jae Chung ◽  
Kishore Pochiraju
Keyword(s):  
Reinforcement Learning ◽  
Robotic Systems ◽  
Multi Agent System ◽  
Learning Agents ◽  
Loosely Coupled ◽  
Reward Function ◽  
Strategy Adaptation ◽  
Agent Learning ◽  
Multi Agent ◽  
Reward Functions

As robotic systems become more prevalent, it is highly desirable for them to be able to operate in highly dynamic environments. A common approach is to use reinforcement learning to allow an agent controlling the robot to learn and adapt its behavior based on a reward function. This paper presents a novel multi-agent system that cooperates to control a single robot battle tank in a melee battle scenario, with no prior knowledge of its opponents’ strategies. The agents learn through reinforcement learning, and are loosely coupled by their reward functions. Each agent controls a different aspect of the robot’s behavior. In addition, the problem of delayed reward is addressed through a time-averaged reward applied to several sequential actions at once. This system was evaluated in a simulated melee combat scenario and was shown to learn to improve its performance over time. This was accomplished by each agent learning to pick specific battle strategies for each different opponent it faced.

scholarly journals Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

2020 ◽  
Vol 17 (2) ◽  
pp. 647-664
Author(s):  
Yangyang Ge ◽  
Fei Zhu ◽  
Wei Huang ◽  
Peiyao Zhao ◽  
Quan Liu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Action Function ◽  
Q Learning ◽  
State Action ◽  
Markov Game ◽  
Safety Constraints ◽  
Multi Agent

Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in the above areas where an agent may fall into dangerous states that are irreversible, causing great damage. To solve the safety problem, in this paper we introduce a Multi-Agent Cooperation Q-Learning Algorithm based on Constrained Markov Game. In this method, safety constraints are added to the set of actions, and each agent, when interacting with the environment to search for optimal values, should be restricted by the safety rules, so as to obtain an optimal policy that satisfies the security requirements. Since traditional Multi-Agent reinforcement learning algorithm is no more suitable for the proposed model in this paper, a new solution is introduced for calculating the global optimum state-action function that satisfies the safety constraints. We take advantage of the Lagrange multiplier method to determine the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, under conditions that the state-action function and the constraint function are both differentiable, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm.

SWARM: A Novel Methodology for Integrated Circuit Layout Automation Based on Principles of Self-organization

2020 ◽  
Author(s):  
Daniel Marolt
Keyword(s):  
Integrated Circuits ◽  
Integrated Circuit ◽  
Design Automation ◽  
Point Of View ◽  
Self Organization ◽  
Multi Agent System ◽  
Self Organized ◽  
Multi Agent ◽  
The Individual ◽  
Place And Route

After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. This book presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), a novel interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic approach, similar to the roundup of a sheep herd, is to let autonomous layout modules interact with each other inside a successively tightened layout zone. Considering various principles of self-organization, remarkable overall solutions can result from the individual, local, selfish actions of the modules. Displaying this fascinating phenomenon of emergence, examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. From an academic point of view, SWARM combines the strengths of procedural generators with the assets of optimization algorithms, thus p...

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