This work introduces defeasible action rules that capture the typical outcome of action, without committing to what happens in exceptional circumstances, and defines an inference operator for defeasible action rules inspired by rational closure, adapted to the dynamic setting.
The Graph-based Reasoning UncErtainty in Trajectories (GRUET) method for the uncertainty quantification of ReAct is presented, comprising turn-level reasoning uncertainty quantification and trajectory-level uncertainty aggregation; the former precisely quantifies reasoning uncertainty via modeling the reasoning space s...
Before a difficult decision, people often act simply to understand the situation better. We turn an object to see another side, place alternatives next to each other, or change one condition and observe what happens. These actions may not complete the task, but they improve the evidence needed for the next choice. LLM-...
The Predictive Justified Perspective (PJP) model is formally defined and proved that it retains the computational efficiency of state-based planning while ensuring logically sound belief reasoning (satisfying the KD45 axioms).
Guang Hu, Weijia Li, Yangmengfei Xu· Proceedings of the Internati...· 0 citations
It is shown that, if the horizon of epistemic actions is bounded, the multiplicative connectives can be eliminated from a logical language with also epistemic and action model modalities.
H. van Ditmarsch, D. Galmiche· Synthese· 0 citations
It is shown a proof-of-the-concept lifted planner can sometimes solve the BPP problem by using a domain-independent heuristic that guides search for a plan.
Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable rewards, particularly in mathematics, code, and formal logic. These settings make model accuracy easier to evaluate and optimize, but it remains unclear how far success u...
Ibrahim Ethem Deveci, Funda Tan Çalık, Barış Deniz Sağlam et al.· 0 citations
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