An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem

Shvo, Maayan; Sohrabi, Shirin; McIlraith, Sheila A.

doi:10.1007/978-3-319-89656-4_23

Cited by 9 publications

(8 citation statements)

References 4 publications

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“…Such probabilistic planners are increasingly integrated into standard software packages for robots and other autonomous agents, e.g., by including some of the most competitive ones in free software for the Robot Operating System (ROS) (Canal et al, 2019). MAP algorithms extend planning algorithms to enable teams of agents (e.g., drone swarms) to cooperate in planning joint actions to achieve shared goals (Shvo, Sohrabi, & McIlraith, 2018;Torreño, Onaindia, Komenda, &Štolba, 2017).…”

Section: Integrated Machine Learning and Probabilistic Planning For Omentioning

confidence: 99%

“…(6) Social skills for MAP and learning. These include capabilities for communicating, collaborating (e.g., agreeing on allocation of tasks), and formulating a joint plan to accomplish shared goals (Shvo et al, 2018;Torreño et al, 2017). Other social skills include recognizing the goals and surmising the beliefs, desires, and intentions of others if they are not explicitly communicated; and learning from others by imitating successful behaviors and by asking for advice, demonstrations, and feedback when in doubt (Daniel, Kroemer, Viering, Metz, & Peters, 2015).…”

Section: Summary: Ai Capabilities For Dealing With Open-world Risks Amentioning

confidence: 99%

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Answerable and Unanswerable Questions in Risk Analysis with Open‐World Novelty

Cox

2020

Risk Analysis

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Decision analysis and risk analysis have grown up around a set of organizing questions: what might go wrong, how likely is it to do so, how bad might the consequences be, what should be done to maximize expected utility and minimize expected loss or regret, and how large are the remaining risks? In probabilistic causal models capable of representing unpredictable and novel events, probabilities for what will happen, and even what is possible, cannot necessarily be determined in advance. Standard decision and risk analysis questions become inherently unanswerable ("undecidable") for realistically complex causal systems with "open-world" uncertainties about what exists, what can happen, what other agents know, and how they will act. Recent artificial intelligence (AI) techniques enable agents (e.g., robots, drone swarms, and automatic controllers) to learn, plan, and act effectively despite open-world uncertainties in a host of practical applications, from robotics and autonomous vehicles to industrial engineering, transportation and logistics automation, and industrial process control. This article offers an AI/machine learning perspective on recent ideas for making decision and risk analysis (even) more useful. It reviews undecidability results and recent principles and methods for enabling intelligent agents to learn what works and how to complete useful tasks, adjust plans as needed, and achieve multiple goals safely and reasonably efficiently when possible, despite open-world uncertainties and unpredictable events. In the near future, these principles could contribute to the formulation and effective implementation of more effective plans and policies in business, regulation, and public policy, as well as in engineering, disaster management, and military and civil defense operations. They can extend traditional decision and risk analysis to deal more successfully with open-world novelty and unpredictable events in large-scale real-world planning, policymaking, and risk management.

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Section: Integrated Machine Learning and Probabilistic Planning For Omentioning

confidence: 99%

Section: Summary: Ai Capabilities For Dealing With Open-world Risks Amentioning

confidence: 99%

Answerable and Unanswerable Questions in Risk Analysis with Open‐World Novelty

Cox

2020

Risk Analysis

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“…While the main focus in classical planning was on producing a single plan, a variety of applications has shown the need for finding a set of plans rather than one. These applications include malware detection (Boddy et al 2005), plan recognition as planning and its applications (Riabov et al 2015;Sohrabi et al 2018;Shvo, Sohrabi, and McIlraith 2018), human team planning (Kim et al 2018), explainable AI (Chakraborti et al 2018), re-planning and plan monitoring (Fox et al 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Naturally, in some application domains diversity plays the major role, while in other quality is the predominant feature. The latter include plan recognition , multi-agent plan recognition (Shvo, Sohrabi, and McIlraith 2018), human team planning (Kim et al 2018), and explainable AI (Chakraborti et al 2018). These applications exploit top-k planners to derive a large number of plans.…”

Section: Introductionmentioning

confidence: 99%

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Top-Quality Planning: Finding Practically Useful Sets of Best Plans

Katz

Sohrabi

Udrea

2020

AAAI

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The need for finding a set of plans rather than one has been motivated by a variety of planning applications. The problem is studied in the context of both diverse and top-k planning: while diverse planning focuses on the difference between pairs of plans, the focus of top-k planning is on the quality of each individual plan. Recent work in diverse planning introduced additionally restrictions on solution quality. Naturally, there are application domains where diversity plays the major role and domains where quality is the predominant feature. In both cases, however, the amount of produced plans is often an artificial constraint, and therefore the actual number has little meaning. Inspired by the recent work in diverse planning, we propose a new family of computational problems called top-quality planning, where solution validity is defined through plan quality bound rather than an arbitrary number of plans. Switching to bounding plan quality allows us to implicitly represent sets of plans. In particular, it makes it possible to represent sets of plans that correspond to valid plan reorderings with a single plan. We formally define the unordered top-quality planning computational problem and present the first planner for that problem. We empirically demonstrate the superior performance of our approach compared to a top-k planner-based baseline, ranging from 41% increase in coverage for finding all optimal plans to 69% increase in coverage for finding all plans of quality up to 120% of optimal plan cost. Finally, complementing the new approach by a complete procedure for generating all valid reorderings of a given plan, we derive a top-quality planner. We show the planner to be competitive with a top-k planner based baseline.

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