An Autonomy Interrogative

Reid, Darryn J.

doi:10.1007/978-3-319-64816-3_21

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…The importance of the homological invariants here relates directly to the previously identified approach for dealing with uncertainty in non-ergodic problem environments by establishing weaker invariant conditions than ergodicity as the basis for reliable decision-making, within known limitations[14]. The context of this previous work was primarily autonomous systems development; however, the homological invariants derived here shows that the approach is applicable to modeling systems manifesting uncertainty more generally.…”

mentioning

confidence: 80%

“…W is the minimum (over all the Morse functions f defined on W) of the number of its critical points C λ (e.g., the sphere 2 number represents the rank/dimension of the nth homology and nth cohomology groups, derived in the next section 14. We can plot this landscape topology in a symmetric fashion, because any smooth (diffeomorphic) local perturbation will leave these principal characteristics invariant.…”

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confidence: 99%

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Tensor-Centric Warfare V: Topology of Systems Confrontation

Ivancevic¹,

Pourbeik²,

Reid³

2019

ICA

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In this paper, as a new contribution to the tensor-centric warfare (TCW) series [1] [2] [3] [4], we extend the kinetic TCW-framework to include non-kinetic effects, by addressing a general systems confrontation [5], which is waged not only in the traditional physical Air-Land-Sea domains, but also simultaneously across multiple non-physical domains, including cyberspace and social networks. Upon this basis, this paper attempts to address a more general analytical scenario using rigorous topological methods to introduce a two-level topological representation of modern armed conflict; in doing so, it extends from the traditional red-blue model of conflict to a red-blue-green model, where green represents various neutral elements as active factions; indeed, green can effectively decide the outcomes from red-blue conflict. System confrontations at various stages of the scenario will be defined by the non-equilibrium phase transitions which are superficially characterized by sudden entropy growth. These will be shown to have the underlying topology changes of the systems-battlespace. The two-level topological analysis of the systems-battlespace is utilized to address the question of topology changes in the combined battlespace. Once an intuitive analysis of the combined battlespace topology is performed, a rigorous topological analysis follows using (co)homological invariants of the combined systems-battlespace manifold.

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confidence: 80%

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confidence: 99%

Tensor-Centric Warfare V: Topology of Systems Confrontation

Ivancevic¹,

Pourbeik²,

Reid³

2019

ICA

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“…Next, to include the Lanchester linear law Equation (2) into Equation ( 3), while keeping their covariance (so that each term represents a vector-field), we need to extend them with quadratic terms of the Lanchester unaimed-fire equations (linear law) as: 4 Red : , Blue : , where the fourth-order tensors…”

Section: Tensor Combat Equationsmentioning

confidence: 99%

Tensor-Centric Warfare II: Entropic Uncertainty Modeling

Ivancevic¹,

Reid²,

Pourbeik³

2018

ICA

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In the first paper of the tensor-centric warfare (TCW) series [1], we proposed a tensor model of combat generalizing earlier Lanchester-type systems with a particular emphasis on contemporary military thinking, including the distributed C4ISR system (Command, Control, Communications, Computing, Intelligence, Surveillance and Reconnaissance). In the present paper, we extend this initial tensor combat model with entropic Lie-derivative machinery in order to capture some aspects of this deep uncertainty, while, in the process, formalizing into our model military notion of symmetry and asymmetry in warfare as a commutator, also known as a Lie bracket. In doing so, we have sought to shift the question from the prediction of outcomes of combat, upon which previous combat models such as the Lanchester-type equations have been typically constructed, towards determining the uncertainty outcomes, using a rigorous analytical basis.

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“…The models used by these agents are typically both incorrect and incomplete. They lack details of how to respond in unpredicted and unpredictable situations, such as when they encounter a situation for which they have no training data or have not been modelled explicitly [30].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Human-Agent Planning for Resilience

Singh¹,

Miller²,

Reid³

2021

Preprint

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Intelligent agents powered by AI planning assist people in complex scenarios, such as managing teams of semi-autonomous vehicles. However, AI planning models may be incomplete, leading to plans that do not adequately meet the stated objectives, especially in unpredicted situations. Humans, who are apt at identifying and adapting to unusual situations, may be able to assist planning agents in these situations by encoding their knowledge into a planner at run-time. We investigate whether people can collaborate with agents by providing their knowledge to an agent using linear temporal logic (LTL) at run-time without changing the agent's domain model. We presented 24 participants with baseline plans for situations in which a planner had limitations, and asked the participants for workarounds for these limitations. We encoded these workarounds as LTL constraints. Results show that participants' constraints improved the expected return of the plans by 10% (p < 0.05) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience. However, participants used more declarative than control constraints over time, but declarative constraints produced plans less similar to the expectation of the participants, which could lead to potential trust issues.

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An Autonomy Interrogative

Cited by 4 publications

References 37 publications

Tensor-Centric Warfare V: Topology of Systems Confrontation

Tensor-Centric Warfare V: Topology of Systems Confrontation

Tensor-Centric Warfare II: Entropic Uncertainty Modeling

Collaborative Human-Agent Planning for Resilience

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