A context-aware approach to automated negotiation using reinforcement learning

Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiation agent depends significantly on the influence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is introduced. Also, a simple negotiation agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against other negotiation agents in the existing literature, it is shown using our context-aware agent that it makes no sense to negotiate without taking context-relevant variables into account. Our context-aware negotiation agent has been implemented in the GENIUS environment, and results obtained are significant and quite revealing.

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Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning

Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19 ◽

10.1145/3357384.3357945 ◽

2019 ◽

Cited By ~ 1

Author(s):

Junqi Zhang ◽

Jiaxin Mao ◽

Yiqun Liu ◽

Ruizhe Zhang ◽

Min Zhang ◽

...

Keyword(s):

Reinforcement Learning ◽

Virtual Environment ◽

Context Aware

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ANEGMA: an automated negotiation model for e-markets

Autonomous Agents and Multi-Agent Systems ◽

10.1007/s10458-021-09513-x ◽

2021 ◽

Vol 35 (2) ◽

Author(s):

Pallavi Bagga ◽

Nicola Paoletti ◽

Bedour Alrayes ◽

Kostas Stathis

Keyword(s):

Reinforcement Learning ◽

Deep Neural Network ◽

Automated Negotiation ◽

Learning To Learn ◽

Negotiation Model ◽

Negotiation Strategies ◽

Exploration Time ◽

Model Free ◽

Bilateral Negotiations ◽

Time Required

AbstractWe present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

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