Predictive Automated Negotiators Employing Risk-Seeking and Risk-Averse Strategies

Masvoula, Marisa; Halatsis, C.; Martakos, Drakoulis

doi:10.1007/978-3-642-23957-1_37

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…For the general multi-issue case, multilayer perceptrons (MLPs) can be used, as is done by Masvoula et al [133]. MLPs are artificial neural networks where some nodes have a nonlinear activation function.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In this case the correlation between the two vectors can be measured, for example by using Eq. (12) [115,132,133].…”

Section: Similarity Between Issue Weightsmentioning

confidence: 99%

“…5.4). [12,84,90] Similarity between bidding strategies [35][36][37]44,45,115,132,133,137,151,153,197] Similarity between preference profiles [12,31,32,84,163,165] To the best of our knowledge there are no accuracy measures for the acceptance strategy and deadline, although some existing measures can be used for these instances as well. We will come back to this when we discuss the other accuracy measures below.…”

Section: Accuracy Measures For Opponent Modelsmentioning

confidence: 99%

“…2. Adapt to the opponent [1,15,28,29,[44][45][46]57,72,73,75,81,82,85,92,133,139,140,150,155,162,173,197,199,204] With the assistance of an opponent model, an agent can adapt to the opponent in multiple ways. One way is to estimate the opponent's reservation value in an attempt to deduce the best possible outcome that the opponent will settle for.…”

Section: Learning About the Opponentmentioning

confidence: 99%

“…Heuristics [7][8][9]30,[75][76][77]163,194,195] 5.4 Bidding strategy Regression analysis Bayesian networks [139] Genetic algorithms [151] Non-linear regression [1,28,73,85,153,161,204] Polynomial interpolation [153] Time series forecasting Derivatives [29,137] Markov chains [140] Neural networks [35,36,115,133,146,151,153,160] Signal processing [44][45][46]132,150,197,199] …”

Section: Learning the Acceptance Strategymentioning

confidence: 99%

See 4 more Smart Citations

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Baarslag

Hendrikx

Hindriks

et al. 2015

Auton Agent Multi-Agent Syst

123

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A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent's preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other's wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.

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Section: Artificial Neural Networkmentioning

confidence: 99%

“…In this case the correlation between the two vectors can be measured, for example by using Eq. (12) [115,132,133].…”

Section: Similarity Between Issue Weightsmentioning

confidence: 99%

Section: Accuracy Measures For Opponent Modelsmentioning

confidence: 99%

Section: Learning About the Opponentmentioning

confidence: 99%

Section: Learning the Acceptance Strategymentioning

confidence: 99%

See 3 more Smart Citations

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Baarslag

Hendrikx

Hindriks

et al. 2015

Auton Agent Multi-Agent Syst

123

View full text Add to dashboard Cite

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Background

Baarslag¹

2016

Exploring the Strategy Space of Negotiating Agents

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Multi-Issue Bargaining With Deep Reinforcement Learning

Chang

2020

Preprint

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Negotiation is a process where agents aim to work through disputes and maximize their surplus. As the use of deep reinforcement learning in bargaining games is unexplored, this paper evaluates its ability to exploit, adapt, and cooperate to produce fair outcomes, in comparison to classical game theoretic results.Two actor-critic networks were trained for the bidding and acceptance strategy, against time-based agents, behavior-based agents, and through selfplay. Gameplay against these agents reveals three key findings. 1) Neural agents learn to exploit time-based agents, achieving clear transitions in decision preference values. The Cauchy distribution emerges as suitable for sampling offers, due to its peaky center and heavy tails. The kurtosis and variance sensitivity of the probability distributions used for continuous control produce trade-offs in exploration and exploitation. 2) Neural agents demonstrate adaptive behavior against different combinations of concession, discount factors, and behavior-based strategies. 3) Most importantly, neural agents learn to cooperate with other behavior-based agents, in certain cases utilizing non-credible threats to force fairer results. This bears similarities with reputation-based strategies in the evolutionary dynamics, and departs from equilibria in classical game theory.

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Predictive Automated Negotiators Employing Risk-Seeking and Risk-Averse Strategies

Cited by 3 publications

References 16 publications

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Background

Multi-Issue Bargaining With Deep Reinforcement Learning

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