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arXiv:2604.19838 · detector doi_compliance · incontrovertible · 2026-05-20 03:13:19.892106+00:00

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Dinparastdjadid A, Supeene I and Engstrom J. Measuring Surprise in the Wild, 2023. DOI:10.48550/arXiv.2305. 07733. URLhttp://arxiv.org/abs/2305.07733. ArXiv:2305.07733 [cs]. Prepared usingsagej.cls 18 Collective Intelligence XX(X) Appendix Model implementation In the original model 43, the kinematic state of an agentv∈Vis defined asx v ={x v, yv, θv, δv, vv}(based on a bicycle modelf, these arexandypositions, heading angleθ, steering angleδ, and longitudinal velocityv) with the kinematic control statesu v ={a v, ωv}(i.e., longitudinal accelerationaand steering ratew). In this work, we expand upon this by adding the signaling statesγ={γ A, γY }(prompting and yielding signal respectively). We can then define our relevant variables with η,o,s=:{x v,γ v,u v |v∈V} a=:{γ v,u v |v∈V} av =:{γ v,u v}. (A.1) In the following, if it might be unclear if a state comes from, for example,oorη, we use the indicatorsx o,v andx η,v. Based on an implementation of the bicycle modelf K 43 with x′ v =f K(xv,u v,∆t),(A.2) where∆tis our discrete simulation timestep, we can then define thegenerative process’probability functions, which as delta distributionsδcan be seen as being deterministic: bp(η′ |η,a) = Y v∈V δ x′ η,v −f K(xη,v ,u a,v,∆t) δ γ′ η,v −γ a,v δ u′ η,v −u a,v bp(o|η) = Y v∈V δ(x o,v −x η,v)δ(γ o,v −γ η,v)δ(u o,v −u η,v). (A.3) The same can be done for thegenerative modelof agentv, which will include some more uncertainty, though. Specifically, we have for the state transition function 

Evidence payload

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  "reconstructed_doi": "10.48550/arXiv.2305.07733.URLhttp://arxiv.org/abs/2305.07733.ArXiv:2305.07733",
  "ref_index": 63,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
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