Planning with Spatio-Temporal Search Control Knowledge

2018 ◽  
Vol 30 (10) ◽  
pp. 1915-1928 ◽  
Author(s):  
Xu Lu ◽  
Cong Tian ◽  
Zhenhua Duan ◽  
Hongwei Du
Keyword(s):  
Spatio Temporal ◽  
Control Knowledge ◽  
Search Control ◽  
Temporal Search

scholarly journals Temporalising Separation Logic for Planning with Search Control Knowledge

2017 ◽  
Author(s):  
Xu Lu ◽  
Cong Tian ◽  
Zhenhua Duan
Keyword(s):  
Temporal Logic ◽  
Expressive Power ◽  
Separation Logic ◽  
Temporal Constraints ◽  
Ai Planning ◽  
Temporal Logics ◽  
Spatial Constraints ◽  
Spatio Temporal ◽  
Control Knowledge ◽  
Search Control

Temporal logics are widely adopted in Artificial Intelligence (AI) planning for specifying Search Control Knowledge (SCK). However, traditional temporal logics are limited in expressive power since they are unable to express spatial constraints which are as important as temporal ones in many planning domains. To this end, we propose a two-dimensional (spatial and temporal) logic namely PPTL^SL by temporalising separation logic with Propositional Projection Temporal Logic (PPTL). The new logic is well-suited for specifying SCK containing both spatial and temporal constraints which are useful in AI planning. We show that PPTL^SL is decidable and present a decision procedure. With this basis, a planner namely S-TSolver for computing plans based on the spatio-temporal SCK expressed in PPTL^SL formulas is developed. Evaluation on some selected benchmark domains shows the effectiveness of S-TSolver.

scholarly journals Decision-Theoretic Planning with non-Markovian Rewards

2006 ◽  
Vol 25 ◽  
pp. 17-74 ◽  
Author(s):  
S. Thiebaux ◽  
C. Gretton ◽  
J. Slaney ◽  
D. Price ◽  
F. Kabanza
Keyword(s):  
Decision Process ◽  
Single Interface ◽  
Reward Function ◽  
Domain Specific ◽  
Current State ◽  
Markovian Decision Process ◽  
Solution Methods ◽  
Control Knowledge ◽  
Search Control ◽  
Domain Independent

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPP's treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter.

Enabling Spatio-Temporal Search in Open Data

2019 ◽  
Vol 55 ◽  
pp. 21-36 ◽  
Author(s):  
Sebastian Neumaier ◽  
Axel Polleres
Keyword(s):  
Open Data ◽  
Spatio Temporal ◽  
Temporal Search

Topical and Spatio-Temporal Search over Distributed Online Databases

2012 ◽  
pp. 386-403
Author(s):  
Nikos Zotos ◽  
Sofia Stamou
Keyword(s):  
Temporal Orientation ◽  
Similarity Metrics ◽  
Spatial Similarity ◽  
Online Databases ◽  
Geographic Coverage ◽  
Contextual Elements ◽  
Temporal Expressions ◽  
Spatio Temporal ◽  
Database Content ◽  
Temporal Search

In this chapter, the authors propose a novel framework for the support of multi-faceted searches over distributed Web-accessible databases. Towards this goal, the authors introduce a method for analyzing and processing a sample of the database contents in order to deduce the topical, the geographic, and the temporal orientation of the entire database contents. To extract the database topics, the authors apply techniques leveraged from the NLP community. To identify the database geographic footprints, the authors first rely on geographic ontologies in order to extract toponyms from the database content samples and then employ geo-spatial similarity metrics to estimate the geographic coverage of the identified toponyms. Finally, to determine the time aspects associated with the database entities, the authors extract temporal expressions from the entities’ contextual elements and utilize a time ontology against which the temporal similarity between the identified entities is estimated.

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