Deceptive Labeling: Hypergames on Graphs for Stealthy Deception

Kulkarni, Abhishek N.; Luo, Huan; Leslie, Nandi; Kamhoua, Charles A.; Fu, Jie

doi:10.1109/lcsys.2020.3008078

Cited by 13 publications

(11 citation statements)

References 22 publications

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“…However, the game model studied in our paper is not a normal-form game, but instead a game on graph. A hypergame model based on a game on graph has been defined in our previous work (Kulkarni, Luo, Leslie, Kamhoua, & Fu, 2020;Li, Ma, Kulkarni, & Fu, 2020), wherein we study the games with one-sided incomplete information about payoffs in temporal logic. This paper is an extended version of our previous work , which studied the deceptive almost-sure winning with action deception.…”

Section: Related Workmentioning

confidence: 99%

Synthesis of Deceptive Strategies in Reachability Games with Action Misperception

Kulkarni

2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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We consider a class of two-player turn-based zero-sum games on graphs with reachability objectives, known as reachability games, where the objective of Player 1 (P1) is to reach a set of goal states, and that of Player 2 (P2) is to prevent this. In particular, we consider the case where the players have asymmetric information about each other's action capabilities: P2 starts with an incomplete information (misperception) about P1's action set, and updates the misperception when P1 uses an action previously unknown to P2. When P1 is made aware of P2's misperception, the key question is whether P1 can control P2's perception so as to deceive P2 into selecting actions to P1's advantage? To answer this question, we introduce a dynamic hypergame model to capture the reachability game with evolving misperception of P2. Then, we present a fixed-point algorithm to compute the deceptive winning region and strategy for P1 under almost-sure winning condition. Finally, we show that the synthesized deceptive winning strategy is at least as powerful as the (non-deceptive) winning strategy in the game in which P1 does not account for P2's misperception. We illustrate our algorithm using a robot motion planning in an adversarial environment.

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Section: Related Workmentioning

confidence: 99%

Synthesis of Deceptive Strategies in Reachability Games with Action Misperception

Kulkarni

2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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“…We introduce a model of hypergame on graph to integrate the games G 1 of P1 and G 2 of P2 into a single graphical model. Definition 4 (Hypergame on Graph (modified from [17] 3 )). Given the perceptual game arenas G = S, Act, T, AP, L and G 2 = S, Act, T, AP, L 2 ,…”

Section: Deceptive Synthesis: Hypergames On Graphsmentioning

confidence: 99%

“…A player receives a payoff equal to one if the labeling over the outcome (state-sequence) of the game satisfies its temporal logic formula. In our recent work [17], we have shown that a class of decoy-based deception can be captured by assuming that the defender has the true labels of game states but the attacker has incorrect labels. For example, a state labeled "unsafe" by the defender may be mislabeled as "safe" for the attacker.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the defender strategy should be stealthy, in the sense that the attacker cannot realize a misperception exists before getting caught by one of the traps. To determine the decoy allocation, we employ the aforementioned solutions of hypergames [17] to calculate the defender's deceptive sure-winning region given each individual decoys. The selection of decoy locations is based on compositional synthesis [10,18], which answers, given the two deceptive sure-winning regions for decoys allocated at two different states s and s ′ , what is the deceptive sure-winning region when both states are allocated as decoys simultaneously?…”

Section: Introductionmentioning

confidence: 99%

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Decoy Allocation Games on Graphs with Temporal Logic Objectives

Kulkarni¹,

Fu²,

Luo³

et al. 2020

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We study a class of games, in which the adversary (attacker) is to satisfy a complex mission specified in linear temporal logic, and the defender is to prevent the adversary from achieving its goal. A deceptive defender can allocate decoys, in addition to defense actions, to create disinformation for the attacker. Thus, we focus on the problem of jointly synthesizing a decoy placement strategy and a deceptive defense strategy that maximally exploits the incomplete information the attacker about the decoy locations. We introduce a model of hypergames on graphs with temporal logic objectives to capture such adversarial interactions with asymmetric information. Using the hypergame model, we analyze the effectiveness of a given decoy placement, quantified by the set of deceptive winning states where the defender can prevent the attacker from satisfying the attack objective given its incomplete information about decoy locations. Then, we investigate how to place decoys to maximize the defender's deceptive winning region. Considering the large search space for all possible decoy allocation strategies, we incorporate the idea of compositional synthesis from formal methods and show that the objective function in the class of decoy allocation problem is monotone and nondecreasing. We derive the sufficient conditions under which the objective function for the decoy allocation problem is submodular, or supermodular, respectively. We show a sub-optimal allocation can be efficiently computed by iteratively composing the solutions of hypergames with a subset of decoys and the solution of a hypergame given a single decoy. We use a running example to illustrate the proposed method.

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“…In fact, the condition for players to trust their cognition is crucial in games with misperception 24,25 . Particularly, misperception may spoil players' cognition if others' strategies are not consistent with their own anticipation, and moreover, it may even ruin the balance or even lead to collapse of the model 26 .…”

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confidence: 99%

How Does Misperception Affect Zero-Determinant Strategies In Iterated Prisoner’s Dilemma?

Cheng

Chen

Hong

2021

Preprint

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Zero-determinant (ZD) strategies have attracted wide attention in Iterated Prisoner’s Dilemma (IPD) games, since the player equipped with ZD strategies can unilaterally enforce the two players’ expected utilities subjected to a linear relation. On the other hand, uncertainties, which may be caused by misperception, occur in IPD inevitably in practical circumstances. To better understand the situation, we consider the influence of misperception on ZD strategies in IPD, where the two players, player X and player Y , have different cognitions, but player X detects the misperception and it is believed to make ZD strategies by player Y. We provide a necessary and sufficient condition for the ZD strategies in IPD with misperception, where there is also a linear relationship between players’ utilities in player X’s cognition. Then we explore bounds of players’ expected utility deviation from a linear relationship in player X’s cognition with also improving its own utility.

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Deceptive Labeling: Hypergames on Graphs for Stealthy Deception

Cited by 13 publications

References 22 publications

Synthesis of Deceptive Strategies in Reachability Games with Action Misperception

Synthesis of Deceptive Strategies in Reachability Games with Action Misperception

Decoy Allocation Games on Graphs with Temporal Logic Objectives

How Does Misperception Affect Zero-Determinant Strategies In Iterated Prisoner’s Dilemma?

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