scholarly journals Acquiring planning domain models using LOCM

2013 ◽  
Vol 28 (2) ◽  
pp. 195-213 ◽  
Author(s):  
Stephen N. Cresswell ◽  
Thomas L. McCluskey ◽  
Margaret M. West
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Engineering ◽  
Knowledge Bases ◽  
Domain Model ◽  
Learning Object ◽  
Ai Planning ◽  
Automated Generation ◽  
Action Sequences ◽  
Sound Sequence ◽  
Domain Models

AbstractThe problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for artificial intelligence (AI) planning. This paper describes Learning Object-Centred Models (LOCM), a system that carries out the automated generation of a planning domain model from example training plans. The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits assumptions about the kinds of domain model it has to generate, rather than handcrafted clues or planner-oriented knowledge. It assumes that actions change the state of objects, and require objects to be in a certain state before they can be executed. In this paper, we describe the implemented LOCM algorithm, the assumptions that it is based on, and an evaluation using plans generated through goal-directed solutions, through random walk, and through logging human-generated plans for the game of freecell. We analyze the performance of LOCM by its application to the induction of domain models from five domains.

scholarly journals Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm

2021 ◽  
Vol 10 (7) ◽  
pp. 24-32
Author(s):  
Mohamed Elkawkagy* ◽  
Elbeh Heba
Keyword(s):  
Artificial Intelligence ◽  
Search Space ◽  
Processing Technique ◽  
Substantial Improvement ◽  
Domain Model ◽  
Map Reduce ◽  
Planning Problem ◽  
Ai Planning ◽  
Hierarchical Planning ◽  
Artificial Intelligence Planning

While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to divide the hierarchical planning problem into smaller problems, so-called sub-problems. A pre-processing technique is conducted for each sub-problem to reduce a declarative hierarchical planning domain model and then find an individual solution for each so-called sub-problem sub-plan. In the reduction part, the conflict between sub-plans is resolved to provide a general solution plan to the given hierarchical AI-planning problem. Preprocessing phase helps the planner cut off the hierarchical planning search space for each sub-problem by removing the compulsory literal elements that help the hierarchical planner seek a solution. The proposed approach has been fully implemented successfully, and some experimental results findings will be provided as proof of our approach's substantial improvement inefficiency.

scholarly journals The Third Competition on Knowledge Engineering for Planning and Scheduling

AI Magazine ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 95
Author(s):  
Roman Bartak ◽  
Simone Fratini ◽  
Lee McCluskey
Keyword(s):  
Knowledge Engineering ◽  
Formal Description ◽  
Ai Planning ◽  
Application Area ◽  
Planning And Scheduling ◽  
Specific Language ◽  
The Third ◽  
Narrow Focus ◽  
Domain Models ◽  
Language Output

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece in September 2009. We give an overview of how the competition has developed since its first run in 2005, and its relationship with the AI planning field. This run of the competition focused on translators that when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition.

Using artificial intelligence techniques to support project management

1987 ◽  
Vol 1 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Raymond E. Levitt ◽  
John C. Kunz
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Project Management ◽  
Knowledge Engineering ◽  
Project Planning ◽  
Ai Planning ◽  
Engineering Project ◽  
Knowledge Processing ◽  
Domain Specific ◽  
Knowledge Based

AbstractThis paper develops a philosophy for the use of Artificial Intelligence (AI) techniques as aids in engineering project management.First, we propose that traditional domain-independent, ‘means–and’ planners, may be valuable aids for planning detailed subtasks on projects, but that domain-specific planning tools are needed for work package or executive level project planning. Next, we propose that hybrid computer systems, using knowledge processing techniques in conjunction with procedural techniques such as decision analysis and network-based scheduling, can provide valuable new kinds of decision support for project objective-setting and project control, respectively. Finally we suggest that knowledge-based interactive graphics, developed for providing graphical explanations and user control in advanced knowledge processing environments, can provide powerful new kinds of decision support for project management.The first claim is supported by a review and analysis of previous work in the area of automated AI planning techniques. Our experience with PLATFORM I, II and III, a series of prototype AI-leveraged project management systems built using the IntelliCorp Knowledge Engineering Environment (KEE™), provides the justification for the latter two claims.

A Decision Support System for Artificial Recharge Plant

1991 ◽  
Vol 24 (9) ◽  
pp. 331-342 ◽  
Author(s):  
C. Masciopinto ◽  
V. Palmisano ◽  
F. Tangorra ◽  
M. Vurro
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Artificial Recharge ◽  
Groundwater Resources ◽  
Treatment Plant ◽  
Knowledge Bases ◽  
Wastewater Discharge ◽  
Training Tool ◽  
Qualitative And Quantitative ◽  
Expert System Shell

The need for artificial recharge plants is the result of the qualitative and quantitative worsening of groundwater resources due to increased pumping and wastewater discharge. This paper described a system that uses artificial intelligence techniques for designing an artificial recharge plant. The system can be used as a training tool for new engineers, as well as an aid in the choices for expert engineers. The system is an application of an expert system shell running on a common p.c. machine. The model is made up of two knowledge bases, respectively denoted as Quantity artificial recharge and Quality artificial recharge. The former is related to the quantitative aspects, such as geology, climate and land availability, the latter to qualitative aspects, such as water use and treatment plant. Two case studies have been implemented in order to confirm the validity of this kind of systemic approach.

scholarly journals Uncertainty modeling process for semantic technology

2016 ◽  
Vol 2 ◽  
pp. e77 ◽  
Author(s):  
Rommel N. Carvalho ◽  
Kathryn B. Laskey ◽  
Paulo C.G. Da Costa
Keyword(s):  
Knowledge Engineering ◽  
Uncertainty Modeling ◽  
Domain Model ◽  
Proof Of Concept ◽  
Semantic Technology ◽  
Modeling Process ◽  
Ontology Language ◽  
Domain Semantics ◽  
Probabilistic Ontology

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model can be used to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

scholarly journals Artificial intelligence in the synthesis of creative solutions

10.31519/1404 ◽  
2019 ◽  
Author(s):  
Александр Андрейчиков ◽  
Aleksandr Andreychikov ◽  
Ольга Андрейчикова ◽  
Olga Andreichicova
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Present Article ◽  
High Efficiency ◽  
Knowledge Bases ◽  
Computing Technology ◽  
Creative Solutions ◽  
Computer Aided ◽  
Physical Effects ◽  
Technical Systems

Invention problem solving is connected to essential expenses of labour and time, which is spent on the procedures of search and ordering of necessary knowledge, on generation of probable vari-ants of projected systems, on the analysis of offered ideas and de-cisions and understanding perspectiveness of them. The present article outlines the results of the developments in the field of cre-ating computing technology of the synthesis of new engineering on the level of invention. The most attention is paid to problem of computer aided designing on initial stages, where synthesis of new on principal technical systems is carried out. Computer-aided con-struction of new technical system is based on using of data- and knowledge bases of physical effects and of technical decisions as well as different heuristic systematization procedures. The synthe-sis of principles of function of the technical new systems is carried out with using experts knowledge and requires the application of the artificial intelligence methods and the methods of the deci-sions making theory for invention's tasks. Considered approach has been used for synthesis of new technical systems of different functional purposes and had shown high efficiency in computer-aided construction.

scholarly journals Artificial Intelligence in General

2019 ◽  
pp. 1-16
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Design ◽  
Intelligent Systems ◽  
Production Systems ◽  
Knowledge Bases ◽  
Computer Integrated Manufacturing ◽  
Current Status ◽  
Computer Aided ◽  
Control Computer ◽  
History Of

There are many kinds of uses for artificial intelligence (AI) in almost every field. AI is quite often used for control, computer aided design (CAD) and computer aided manufacturing (CAM), machine control, computer integrated manufacturing (CIM), production spot control, factory control, intelligent control, intelligent systems, deep learning, the cloud, knowledge bases, database, management, production systems, statistics, to assist sales forces, environment examination, agriculture, art, livings, daily life, etc. The present AI uses will be reexamined whether there is any matter to be considered further or not in AI research directions and their purposes behind the current status by looking at the history of AI development.

Fundamentals of creating MIVAR expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Autonomous Robots ◽  
Support Systems ◽  
Knowledge Bases ◽  
Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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