Where Should Complexity Go? Cooperation in Complex Agents with Minimal Communication

Bryson, Joanna J.

doi:10.1007/978-3-540-45173-0_23

Cited by 5 publications

(1 citation statement)

References 26 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Relying on an efficient agent model will enable the agent to predict when it is worthwhile to cooperate or share resources and alternatively when it is more advisable to act selfishly or independently of the other teammates. The development of an effective model in bargaining scenarios is therefore also important for MAS design frameworks [ 21 , 22 ]. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining.…”

Section: Introductionmentioning

confidence: 99%

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Mizrahi

Zuckerman

Laufer

2020

Sensors

View full text Add to dashboard Cite

In recent years collaborative robots have become major market drivers in industry 5.0, which aims to incorporate them alongside humans in a wide array of settings ranging from welding to rehabilitation. Improving human–machine collaboration entails using computational algorithms that will save processing as well as communication cost. In this study we have constructed an agent that can choose when to cooperate using an optimal strategy. The agent was designed to operate in the context of divergent interest tacit coordination games in which communication between the players is not possible and the payoff is not symmetric. The agent’s model was based on a behavioral model that can predict the probability of a player converging on prominent solutions with salient features (e.g., focal points) based on the player’s Social Value Orientation (SVO) and the specific game features. The SVO theory pertains to the preferences of decision makers when allocating joint resources between themselves and another player in the context of behavioral game theory. The agent selected stochastically between one of two possible policies, a greedy or a cooperative policy, based on the probability of a player to converge on a focal point. The distribution of the number of points obtained by the autonomous agent incorporating the SVO in the model was better than the results obtained by the human players who played against each other (i.e., the distribution associated with the agent had a higher mean value). Moreover, the distribution of points gained by the agent was better than any of the separate strategies the agent could choose from, namely, always choosing a greedy or a focal point solution. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining. This reward-maximizing strategy selection process based on the SVO can also be potentially applied in other human–machine contexts, including multiagent systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Mizrahi

Zuckerman

Laufer

2020

Sensors

View full text Add to dashboard Cite

show abstract

Animal Models for Computing and Communications

Bales¹,

Kitzmann²

2011

Bio-Inspired Computing and Networking

View full text Add to dashboard Cite

Simplifying the Design of Human-Like Behaviour

Bryson

Tanguy

Creating Synthetic Emotions Through Technological and Robotic Advancements

View full text Add to dashboard Cite

Human intelligence requires decades of full-time training before it can be reliably utilised in modern economies. In contrast, AI agents must be made reliable but interesting in relatively short order. Realistic emotion representations are one way to ensure that even relatively simple specifications of agent behaviour will be expressed with engaging variation, and those social and temporal contexts can be tracked and responded to appropriately. We describe a representation system for maintaining an interacting set of durative states to replicate emotional control. Our model, the Dynamic Emotion Representation (DER), integrates emotional responses and keeps track of emotion intensities changing over time. The developer can specify an interacting network of emotional states with appropriate onsets, sustains and decays. The levels of these states can be used as input for action selection, including emotional expression. We present both a general representational framework and a specific instance of a DER network constructed for a virtual character. The character’s DER uses three types of emotional state as classified by duration timescales, keeping with current emotional theory. We demonstrate the system with a virtual actor. We also demonstrate how even a simplified version of this representation can improve goal arbitration in autonomous agents.

show abstract

Where Should Complexity Go? Cooperation in Complex Agents with Minimal Communication

Cited by 5 publications

References 26 publications

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Animal Models for Computing and Communications

Simplifying the Design of Human-Like Behaviour

Contact Info

Product

Resources

About