Planning to Chronicle

Rahmani, Hazhar; Shell, Dylan A.; O’Kane, Jason M.

doi:10.1007/978-3-030-66723-8_17

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…This emphasizes an important distinction between language-and automata-based properties (cf. our [5]), with fairly subtle implications for our model and approach. Itineraries will be taken to describe sequences of edges and to relate those descriptions to the world's structure, we will form a product.…”

Section: Related Workmentioning

confidence: 91%

Sensor selection for detecting deviations from a planned itinerary

Rahmani¹,

Shell²,

O’Kane³

2021

Preprint

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Suppose an agent asserts that it will move through an environment in some way. When the agent executes its motion, how does one verify the claim? The problem arises in a range of contexts including in validating safety claims about robot behavior, applications in security and surveillance, and for both the conception and the (physical) design and logistics of scientific experiments. Given a set of feasible sensors to select from, we ask how to choose sensors optimally in order to ensure that the agent's execution does indeed fit its predisclosed itinerary. Our treatment is distinguished from prior work in sensor selection by two aspects: the form the itinerary takes (a regular language of transitions) and that families of sensor choices can be grouped as a single choice. Both are intimately tied together, permitting construction of a product automaton because the same physical sensors (i.e., the same choice) can appear multiple times. This paper establishes the hardness of sensor selection for itinerary validation within this treatment, and proposes an exact algorithm based on an ILP formulation that is capable of solving problem instances of moderate size. We demonstrate its efficacy on small-scale case studies, including one motivated by wildlife tracking.

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Section: Related Workmentioning

confidence: 91%

Sensor selection for detecting deviations from a planned itinerary

Rahmani¹,

Shell²,

O’Kane³

2021

Preprint

Self Cite

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“…A preliminary version of this paper (Rahmani et al, 2020) appeared in the 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR-XIV). The following results are new to this version:• We generalize the event model, introducing an occurrence probability for each state-event pair.• We address the case in which no policy can guarantee that the robot will successfully capture a desired story.• We discuss several special cases, showing how they can be solved more efficiently than the general problem.• We added new case studies comparing our general approach to both a greedy baseline and to our specialized algorithms for the newly-introduced special cases.…”

Section: Motivation and Introductionmentioning

confidence: 99%

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confidence: 99%

Planning to chronicle: Optimal policies for narrative observation of unpredictable events

Rahmani

Shell

O’Kane

2022

The International Journal of Robotics Research

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One important class of applications entails a robot scrutinizing, monitoring, or recording the evolution of an uncertain time-extended process. This sort of situation leads to an interesting family of active perception problems that can be cast as planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to. The distinguishing characteristic of this setting is that the robot has influence over what it captures via its sensors, but exercises no causal authority over the process evolving in the world. As such, the robot’s objective is to observe the underlying process and to produce a “chronicle” of occurrent events, subject to a goal specification of the sorts of event sequences that may be of interest. This paper examines variants of such problems in which the robot aims to collect sets of observations to meet a rich specification of their sequential structure. We study this class of problems by modeling a stochastic process via a variant of a hidden Markov model and specify the event sequences of interest as a regular language, developing a vocabulary of “mutators” that enable sophisticated requirements to be expressed. Under different suppositions on the information gleaned about the event model, we formulate and solve different planning problems. The core underlying idea is the construction of a product between the event model and a specification automaton. Using this product, we compute a policy that minimizes the expected number of steps to reach a goal state. We introduce a general algorithm for this problem as well as several more efficient algorithms for important special cases. The paper reports and compares performance metrics by drawing on some small case studies analyzed in depth via simulation. Specifically, we study the effect of the robot’s observation model on the average time required for the robot to record a desired story. We also compare our algorithm with a baseline greedy algorithm, showing that our algorithm outperforms the greedy algorithm in terms of the average time to record a desired story. In addition, experiments show that the algorithms tailored to specialized variants of the problem are rather more efficient than the general algorithm.

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