Synthesis of Deceptive Strategies in Reachability Games with Action Misperception

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? We show that there might exist a deceptive winning strategy for P1 that ensures P1's objective is achieved with probability one from a state otherwise losing for P1, had the information being symmetric and complete. We present three key results: First, we introduce a dynamic hypergame model to capture the reachability game with evolving misperception of P2. Second, we present a fixed-point algorithm to compute the Deceptive Almost-Sure Winning (DASW) region and DASW strategy. Finally, we show that DASW strategy is at least as powerful as Almost-Sure Winning (ASW) 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.Comment: 7 pages, 4 figures, submitted to IJCAI 202

Opportunistic Synthesis in Reactive Games under Information Asymmetry

Reactive synthesis is a class of methods to construct a provably-correct control system, referred to as a robot, with respect to a temporal logic specification in the presence of a dynamic and uncontrollable environment. This is achieved by modeling the interaction between the robot and its environment as a two-player zero-sum game. However, existing reactive synthesis methods assume both players to have complete information, which is not the case in many strategic interactions. In this paper, we use a variant of hypergames to model the interaction between the robot and its environment; which has incomplete information about the specification of the robot. This model allows us to identify a subset of game states from where the robot can leverage the asymmetrical information to achieve a better outcome, which is not possible if both players have symmetrical and complete information. We then introduce a novel method of opportunistic synthesis by defining a Markov Decision Process (MDP) using the hypergame under temporal logic specifications. When the environment plays some stochastic strategy in its perceived sure-winning and sure-losing regions of the game, we show that by following the opportunistic strategy, the robot is ensured to only improve the outcome of the game - measured by satisfaction of sub-specifications - whenever an opportunity becomes available. We demonstrate the correctness and optimality of this method using a robot motion planning example in the presence of an adversary.Comment: Submitted to Conference on Decision and Control 201

Synthesizing Attack-Aware Control and Active Sensing Strategies under Reactive Sensor Attacks

We consider the probabilistic planning problem for a defender (P1) who can jointly query the sensors and take control actions to reach a set of goal states while being aware of possible sensor attacks by an adversary (P2) who has perfect observations. To synthesize a provably correct, attack-aware control and active sensing strategy for P1, we construct a stochastic game on graph where the augmented state includes the actual game state (known by the attacker), the belief of the defender about the game state (constructed by the attacker given the attacker's information about the defender's information). We presented an algorithm to solve a belief-based, randomized strategy for P1 to ensure satisfying P1's reachability objective with probability one, under the worst case sensor attacks carried out by an informed P2. The correctness of the algorithm is proven and illustrated with an example.Comment: 6 pages, 1 figure, 1 table, 1 algorith

Experiences of sharing results of community based serosurvey with participants in a district of Maharashtra, India.

A growing number of organisations, including medical associations, recommend that research subjects should be given the option of being informed about the general outcome and results of the study. We recently completed a study involving nine serosurveys from 2018 to 2020 in five districts of India among three age groups (children 9 months to < 5 years; 5 to < 15 years of age, and women 15 to < 50 years of age before and after the measles and rubella (MR) vaccination campaigns). In Palghar district of Maharashtra all individuals in 30 selected clusters were enumerated, and 13 individuals per age group were randomly sampled. We established the procedures to return the results to the respondents for each stage of the survey. Of the 1,166 individuals selected for the measles and rubella serosurvey, 971 (83%) agreed to participate and were enrolled. Participants were informed that they will only be contacted if they test seronegative for measles and/or rubella antibodies. Overall, 140 individuals enrolled in the survey tested seronegative for IgG antibodies to measles and/or rubella viruses; were provided the reports and informed to seek medical advice. Upon follow up by phone, 10% (14) of the 140 participants reported to have been vaccinated. In this paper we discuss the procedures, experiences and considerations in returning results to participants in a community-based measles and rubella serosurvey. Although the lessons learned are specific to post measles-rubella vaccine campaign serosurvey in India, they might be helpful to those contemplating sharing results to participants of large scale survey settings