A Game Model with Private Goal and Belief

Wu, Guihua; Luo, Xudong; Zhong, Qiaoting

doi:10.1007/978-3-319-13560-1_22

Cited by 3 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given a frame of discernment Θ = { 1 , … , }, where is a possible payoff of evaluating a set of outcomes and 1 < … < , let be a choice of a given player, which corresponds to a mass function over Θ. 1 By the concept of mass function, the point-valued expected payoff formula can be generalized to the interval-valued expected payoff defined as follows 20 :…”

Section: Basics Of D-s Theorymentioning

confidence: 99%

“…Generally speaking, decision making involved multiple self‐interested agents is more complicated than that of only one agent. This is because when multiple agents make decisions, they have to consider how other agents choose their strategies . The most fundamental solution concept in multi‐agents decision making (i.e., game theory) is Nash equilibrium, which is widely used for predicting the outcome of a strategic interaction in different research areas .…”

Section: Introductionmentioning

confidence: 99%

“…other agents choose their strategies. [1][2][3][4] The most fundamental solution concept in multi-agents decision making (i.e., game theory) is Nash equilibrium, 5 which is widely used for predicting the outcome of a strategic interaction in different research areas. 2,[6][7][8] Informally, a Nash equilibrium corresponds to a steady state of a matrix game (i.e., each player has no incentive to deviate the state).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Matrix games with missing, interval, and ambiguous lottery payoffs of pure strategy profiles and compound strategy profiles

Luo

Jiang

2017

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

In a matrix game, the interactions among players are based on the assumption that each player has accurate information about the payoffs of their interactions and the other players are rationally self‐interested. As a result, the players should definitely take Nash equilibrium strategies. However, in real‐life, when choosing their optimal strategies, sometimes the players have to face missing, imprecise (i.e., interval), ambiguous lottery payoffs of pure strategy profiles and even compound strategy profile, which means that it is hard to determine a Nash equilibrium. To address this issue, in this paper we introduce a new solution concept, called ambiguous Nash equilibrium, which extends the concept of Nash equilibrium to the one that can handle these types of ambiguous payoff. Moreover, we will reveal some properties of matrix games of this kind. In particular, we show that a Nash equilibrium is a special case of ambiguous Nash equilibrium if the players have accurate information of each player's payoff sets. Finally, we give an example to illustrate how our approach deals with real‐life game theory problems.

show abstract

Section: Basics Of D-s Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Matrix games with missing, interval, and ambiguous lottery payoffs of pure strategy profiles and compound strategy profiles

Luo

Jiang

2017

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To model and explain the various interactive decision-making scenarios in social and economic activities [12,13], different game-theory models have been studied. A typical game model is extensive games [14][15][16][17]; this model is used in sequential decision-making (SDM) scenarios [18]. In an extensive game, players take turns to choose actions; thus, a game tree is normally used to represent the process of an extensive game.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Playing Extensive Games with Learning of Opponent’s Cognition

Liu,

Cong,

Yao

et al. 2024

Sensors

View full text Add to dashboard Cite

Decision-making is a basic component of agents’ (e.g., intelligent sensors) behaviors, in which one’s cognition plays a crucial role in the process and outcome. Extensive games, a class of interactive decision-making scenarios, have been studied in diverse fields. Recently, a model of extensive games was proposed in which agent cognition of the structure of the underlying game and the quality of the game situations are encoded by artificial neural networks. This model refines the classic model of extensive games, and the corresponding equilibrium concept—cognitive perfect equilibrium (CPE)—differs from the classic subgame perfect equilibrium, since CPE takes agent cognition into consideration. However, this model neglects the consideration that game-playing processes are greatly affected by agents’ cognition of their opponents. To this end, in this work, we go one step further by proposing a framework in which agents’ cognition of their opponents is incorporated. A method is presented for evaluating opponents’ cognition about the game being played, and thus, an algorithm designed for playing such games is analyzed. The resulting equilibrium concept is defined as adversarial cognition equilibrium (ACE). By means of a running example, we demonstrate that the ACE is more realistic than the CPE, since it involves learning about opponents’ cognition. Further results are presented regarding the computational complexity, soundness, and completeness of the game-solving algorithm and the existence of the equilibrium solution. This model suggests the possibility of enhancing an agent’s strategic ability by evaluating opponents’ cognition.

show abstract