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.

scholarly journals An Admissible HTN Planning Heuristic

2017 ◽  
Author(s):  
Pascal Bercher ◽  
Gregor Behnke ◽  
Daniel Höller ◽  
Susanne Biundo
Keyword(s):  
Search Space ◽  
Planning System ◽  
Hierarchical Task Network ◽  
Planning Approach ◽  
Htn Planning ◽  
Efficient Planning ◽  
Being There ◽  
Control Knowledge ◽  
Search Control ◽  
Domain Independent

Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.

scholarly journals Synthesizing Customized Planners from Specifications

1998 ◽  
Vol 8 ◽  
pp. 93-128 ◽  
Author(s):  
B. Srivastava ◽  
S. Kambhampati
Keyword(s):  
Artificial Intelligence ◽  
State Space ◽  
Costs And Benefits ◽  
Domain Specific ◽  
Planning And Control ◽  
Control Knowledge ◽  
Automated Software ◽  
And Control ◽  
Domain Independent ◽  
Synthesis Approach

Existing plan synthesis approaches in artificial intelligence fall into two categories -- domain independent and domain dependent. The domain independent approaches are applicable across a variety of domains, but may not be very efficient in any one given domain. The domain dependent approaches need to be (re)designed for each domain separately, but can be very efficient in the domain for which they are designed. One enticing alternative to these approaches is to automatically synthesize domain independent planners given the knowledge about the domain and the theory of planning. In this paper, we investigate the feasibility of using existing automated software synthesis tools to support such synthesis. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used to derive a domain-customized planner through a semi-automatic combination of a declarative theory of planning, and the declarative control knowledge specific to a given domain, to semi-automatically combine them to derive domain-customized planners. We discuss what it means to write a declarative theory of planning and control knowledge for KIDS, and illustrate our approach by generating a class of domain-specific planners using state space refinements. Our experiments show that the synthesized planners can outperform classical refinement planners (implemented as instantiations of UCP, Kambhampati & Srivastava, 1995), using the same control knowledge. We will contrast the costs and benefits of the synthesis approach with conventional methods for customizing domain independent planners.

Heuristische Textpraktiken in den Wissenschaften

2020 ◽  
Vol 48 (1) ◽  
pp. 1-46
Author(s):  
Michael Bender ◽  
Marcus Müller
Keyword(s):  
Science Research ◽  
Annotation Scheme ◽  
Scientific Debate ◽  
Domain Specific ◽  
Current State ◽  
Scientific Disciplines ◽  
Textual Practices ◽  
Collaborative Annotation ◽  
The Subject ◽  
State Of Research

AbstractThis article contains a comparative study of heuristic textual practices in various scientific disciplines. By this we mean formulation practices with which new knowledge is generated in institutionally influenced routines and connected to existing knowledge, e. g. ‚highlighting the relevance of a research topic‘, ‚defining a concept‘ or ‚supporting a statement argumentatively‘.The aim is to find out to what extent such textual practices occur in different scientific disciplines, how they are distributed and combined. Furthermore, we study the effects domain-specific contexts have on heuristic textual practices. The data basis of our study is a corpus of 65 dissertations from the 13 different faculties of the TU Darmstadt. In the pilot study we report here, we examined the introductory chapters of the dissertations. Methodologically, it is an annotation study: Based on the current state of research on the subject, we have derived a basic annotation scheme, which we have developed and refined in a collaborative process of guideline creation. Our study affiliates on socio-pragmatic research on text production and formulation routines in the sciences. It is theoretically informed by the philosophy of science research on heuristics, methodically we make a contribution to the scientific debate on collaborative annotation procedures.

Conformal elastohydrodynamic lubrication analysis for engine bearing design: A brief review

2010 ◽  
Vol 224 (12) ◽  
pp. 2648-2653 ◽  
Author(s):  
J F Booker ◽  
S Boedo ◽  
D Bonneau
Keyword(s):  
Elastohydrodynamic Lubrication ◽  
Physical Models ◽  
Large Sample ◽  
Current State ◽  
Solution Methods ◽  
Bearing Design ◽  
Engine Bearing

The history and current state of conformal elastohydrodynamic lubrication analysis for engine bearing design is reviewed through a large sample of its published literature. Major themes of the physical models and solution methods are examined, together with attention to important special categories of current research.

A Transformer-Based Approach to Multilingual Fake News Detection in Low-Resource Languages

2022 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
Arkadipta De ◽  
Dibyanayan Bandyopadhyay ◽  
Baban Gain ◽  
Asif Ekbal
Keyword(s):  
Language Processing ◽  
English Language ◽  
Neural Model ◽  
Fake News ◽  
Web Content ◽  
Low Resource ◽  
Domain Specific ◽  
Cross Domain ◽  
Current State ◽  
Domain Transfer

Fake news classification is one of the most interesting problems that has attracted huge attention to the researchers of artificial intelligence, natural language processing, and machine learning (ML). Most of the current works on fake news detection are in the English language, and hence this has limited its widespread usability, especially outside the English literate population. Although there has been a growth in multilingual web content, fake news classification in low-resource languages is still a challenge due to the non-availability of an annotated corpus and tools. This article proposes an effective neural model based on the multilingual Bidirectional Encoder Representations from Transformer (BERT) for domain-agnostic multilingual fake news classification. Large varieties of experiments, including language-specific and domain-specific settings, are conducted. The proposed model achieves high accuracy in domain-specific and domain-agnostic experiments, and it also outperforms the current state-of-the-art models. We perform experiments on zero-shot settings to assess the effectiveness of language-agnostic feature transfer across different languages, showing encouraging results. Cross-domain transfer experiments are also performed to assess language-independent feature transfer of the model. We also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains that could be useful for the research and development in resource-scarce scenarios.

Mining Domain Terminologies Using Search Engine's Query Log

2021 ◽  
Vol 20 (6) ◽  
pp. 1-32
Author(s):  
Weijian Ni ◽  
Tong Liu ◽  
Qingtian Zeng ◽  
Nengfu Xie
Keyword(s):  
Language Processing ◽  
Heterogeneous Network ◽  
Transductive Learning ◽  
Query Log ◽  
Domain Specific ◽  
Novel Approach ◽  
Commercial Search Engine ◽  
Domain Expertise ◽  
Traditional Approaches ◽  
Domain Independent

Domain terminologies are a basic resource for various natural language processing tasks. To automatically discover terminologies for a domain of interest, most traditional approaches mostly rely on a domain-specific corpus given in advance; thus, the performance of traditional approaches can only be guaranteed when collecting a high-quality domain-specific corpus, which requires extensive human involvement and domain expertise. In this article, we propose a novel approach that is capable of automatically mining domain terminologies using search engine's query log—a type of domain-independent corpus of higher availability, coverage, and timeliness than a manually collected domain-specific corpus. In particular, we represent query log as a heterogeneous network and formulate the task of mining domain terminology as transductive learning on the heterogeneous network. In the proposed approach, the manifold structure of domain-specificity inherent in query log is captured by using a novel network embedding algorithm and further exploited to reduce the need for the manual annotation efforts for domain terminology classification. We select Agriculture and Healthcare as the target domains and experiment using a real query log from a commercial search engine. Experimental results show that the proposed approach outperforms several state-of-the-art approaches.

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