English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Problems arise when using reward functions to capture dependencies betweensequential time-constrained goal states because the state-space must beprohibitively expanded to accommodate a history of successfully achievedsub-goals. Also, policies and value functions derived with stationarityassumptions are not readily decomposable, leading to a tension between rewardmaximization and task generalization. We demonstrate a logic-compatibleapproach using model-based knowledge of environment dynamics and deadlineinformation to directly infer non-stationary policies composed of reusablestationary policies. The policies are constructed to maximize the probabilityof satisfying time-sensitive goals while respecting time-varying obstacles. Ourapproach explicitly maintains two different spaces, a high-level logical taskspecification where the task-variables are grounded onto the low-levelstate-space of a Markov decision process. Computing satisfiability at thetask-level is made possible by a Bellman-like equation which operates on atensor that links the temporal relationship between the two spaces; theequation solves for a value function that can be explicitly interpreted as theprobability of sub-goal satisfaction under the synthesized non-stationarypolicy, an approach we term Constraint Satisfaction Propagation (CSP).

Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for Temporal Logic Planning