Stackelberg Planning: Towards Effective Leader-Follower State Space Search

Speicher, Patrick; Steinmetz, Marcel; Backes, Michael; Hoffmann, Jörg; Künnemann, Robert

doi:10.1609/aaai.v32i1.12090

Cited by 18 publications

(26 citation statements)

References 17 publications

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“…Pozanco et al (2019) introduced the problem of identifying a state that would, given a set of possible goals, minimize the average distance to the goals, or the maximum distance to any goal, which could be thought of as having positive effects on future goals. Another concept, related to having negative effects on others, is Stackelberg planning (Speicher et al 2018), "where a leader player in a classical planning task chooses a minimum-cost action sequence aimed at maximizing the plan cost of a follower player in the same task. "…”

Section: Related Workmentioning

confidence: 99%

Planning to Avoid Side Effects

Klassen

McIlraith

Muise

et al. 2022

AAAI

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In sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes -- a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally.

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Section: Related Workmentioning

confidence: 99%

Planning to Avoid Side Effects

Klassen

McIlraith

Muise

et al. 2022

AAAI

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“…Stackelberg tasks are solved by performing a search in the space of leader actions, and solving the corresponding follower subtask at every node. Previous work by Speicher et al (2018a) proposed to use iterative-deepening search (IDS) on the leader state space. This allows for a simple but important optimization, caching the plan of the parent and testing whether the same plan is a solution for any successor.…”

Section: Stackelberg Planningmentioning

confidence: 99%

“…Stackelberg planning (Speicher et al 2018a) is a recent framework inspired by Stackelberg security games (Tambe 2011;Shieh et al 2014). It models a single exchange of adversarial plan choice between two agents, leader and follower, acting in the same planning task.…”

Section: Introductionmentioning

confidence: 99%

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Faster Stackelberg Planning via Symbolic Search and Information Sharing

Torralba

Speicher

Künnemann

et al. 2021

AAAI

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Stackelberg planning is a recent framework where a leader and a follower each choose a plan in the same planning task, the leader's objective being to maximize plan cost for the follower. This formulation naturally captures security-related (leader=defender, follower=attacker) as well as robustness-related (leader=adversarial event, follower=agent) scenarios. Solving Stackelberg planning tasks requires solving many related planning tasks at the follower level (in the worst case, one for every possible leader plan). Here we introduce new methods to tackle this source of complexity, through sharing information across follower tasks. Our evaluation shows that these methods can significantly reduce both the time needed to solve follower tasks and the number of follower tasks that need to be solved in the first place.

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“…From the planning side, Speicher et al (2018) used the game-theoretic framework of Stackelberg games for generating robust plans against actions of the adversary. In a similar spirit, Plan Interdiction Games have been proposed to describe the problem of attackers and defenders, where the former plans to intrude a computer network, while the latter tries to prohibit attackers' actions (Letchford and Vorobeychik 2013;Vorobeychik and Pritchard 2020).…”

Section: Introductionmentioning

confidence: 99%

Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation

Chrpa

Rytíř

Horčı́k

et al. 2022

AAAI

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Effective decision making while competing for limited resources in adversarial environments is important for many real-world applications (e.g. two Taxi companies competing for customers). Decision-making techniques such as Automated planning have to take into account possible actions of adversary (or competing) agents. That said, the agent should know what the competitor will likely do and then generate its plan accordingly. In this paper we propose a novel approach for estimating strategies of the adversary (or the competitor), sampling its actions that might hinder agent's goals by interfering with the agent's actions. The estimated competitor strategies are used in plan generation such that agent's actions have to be applied prior to the ones of the competitor, whose estimated times dictate the deadlines. We empirically evaluate our approach leveraging sampling of competitor's actions by comparing it to the naive approach optimising the make-span (not taking the competing agent into account at all) and to Nash Equilibrium (mixed) strategies.

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Stackelberg Planning: Towards Effective Leader-Follower State Space Search

Cited by 18 publications

References 17 publications

Planning to Avoid Side Effects

Planning to Avoid Side Effects

Faster Stackelberg Planning via Symbolic Search and Information Sharing

Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation

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