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.

Comparing HTN Planning Method With Grammar-Based Approach in Generative Design

2000 ◽  
Author(s):  
Hai Shi ◽  
Linda C. Schmidt
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Design ◽  
Planning Method ◽  
Future Research ◽  
Planning Problem ◽  
Generative Design ◽  
Artificial Intelligence Planning ◽  
Design Alternatives ◽  
Htn Planning ◽  
Initial Comparison

Abstract In mechanical conceptual design, the more design alternatives generated, the higher the benefit to designers. In this paper we explore the use of HTN planning, an artificial intelligence planning method, to perform generative conceptual design. The HTN planning method is “goal driven” while the grammar method is “feasibility driven”. We mapped a grammar-based generative method for conceptual design of Meccano carts into an HTN planning problem format. An initial comparison of the two methods is provided in this paper. Exploring the use of a planning method provides a benchmark for future research in generative design.

Pipesworld: Applying Planning Systems to Pipeline Transportation

2004 ◽  
Author(s):  
Ruy L. Milidiu´ ◽  
Frederico dos Santos Liporace
Keyword(s):  
Artificial Intelligence ◽  
Transportation Problem ◽  
General Purpose ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Due Dates ◽  
Planning Systems ◽  
Pipeline Transportation ◽  
Specific Knowledge ◽  
Artificial Intelligence Planning

Most transportation problems consist of moving carriers of stationary cargo. Pipelines are unique in the sense that they are stationary carriers of moving cargo. As a consequence, the planning problem of these systems has singularities that make it very challenging. In this paper we present the Pipesworld model, a transportation problem inspired by the transportation of petroleum derivatives in Petrobras’ pipelines. Pipesworld takes into account important features like product interface constraints, limited product storage capacities and due dates for product delivery. The relevance and unique characteristics of Pipesworld has been recognized by the Artificial Intelligence planning community. Pipesworld has been selected as one of the benchmark problems to be used in the Fourth International Planning Competition, a biannual event to benchmark the state-of-the-art general purpose artificial planning systems. We report the results obtained by general purpose artificial intelligence planning systems when applied to the Pipesworld instances. We also analyze how different modelling techniques may be used to significantly improve the planners’ performance. Although the basic algorithms of these planners do not incorporate any specific knowledge about the pipeline transportation problem, the results obtained so far are quite satisfactory. We also describe our current work in developing Plumber, a dedicated solver, aimed to tackle effective operational situations. Plumber uses general purpose planning techniques but also incorporates domain specific knowledge and may work together with a human expert during the planning process. By applying Plumber to the Pipesworld instances, we compare its performance against general purpose planning systems. Preliminary tests with a first version of Plumber shows that it already outperforms Fast-Forward (FF), one of the best available general purpose planning systems. This shows that improved versions of Plumber have the potential to effectively deal with pipeline transportation operational scenarios.

scholarly journals Multi-Agent Planning for Automatic Geospatial Web Service Composition in Geoportals

2018 ◽  
Vol 7 (10) ◽  
pp. 404 ◽  
Author(s):  
Mahdi Farnaghi ◽  
Ali Mansourian
Keyword(s):  
Web Services ◽  
Web Service ◽  
Spatial Data ◽  
Execution Time ◽  
Search Space ◽  
Planning Problem ◽  
Ai Planning ◽  
Simulation Environment ◽  
Multi Agent ◽  
Open Geospatial Consortium

Automatic composition of geospatial web services increases the possibility of taking full advantage of spatial data and processing capabilities that have been published over the internet. In this paper, a multi-agent artificial intelligence (AI) planning solution was proposed, which works within the geoportal architecture and enables the geoportal to compose semantically annotated Open Geospatial Consortium (OGC) Web Services based on users’ requirements. In this solution, the registered Catalogue Service for Web (CSW) services in the geoportal along with a composition coordinator component interact together to synthesize Open Geospatial Consortium Web Services (OWSs) and generate the composition workflow. A prototype geoportal was developed, a case study of evacuation sheltering was implemented to illustrate the functionality of the algorithm, and a simulation environment, including one hundred simulated OWSs and five CSW services, was used to test the performance of the solution in a more complex circumstance. The prototype geoportal was able to generate the composite web service, based on the requested goals of the user. Additionally, in the simulation environment, while the execution time of the composition with two CSW service nodes was 20 s, the addition of new CSW nodes reduced the composition time exponentially, so that with five CSW nodes the execution time reduced to 0.3 s. Results showed that due to the utilization of the computational power of CSW services, the solution was fast, horizontally scalable, and less vulnerable to the exponential growth in the search space of the AI planning problem.

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.

Chapter 19. Planning and SAT

2021 ◽  
Author(s):  
Jussi Rintanen
Keyword(s):  
Artificial Intelligence ◽  
Model Checking ◽  
Bounded Model Checking ◽  
Planning Problem ◽  
Ai Planning ◽  
Transition Systems ◽  
Computer Aided ◽  
Sat Solver ◽  
Main Ideas ◽  
Planning Problems

The planning problem in Artificial Intelligence was the first application of SAT to reasoning about transition systems and a direct precursor to the use of SAT in a number of other applications, including bounded model-checking in computer-aided verification. This chapter presents the main ideas about encoding goal reachability problems as a SAT problem, including parallel plans and different forms of constraints for speeding up SAT solving, as well as algorithms for solving the AI planning problem with a SAT solver. Finally, more general planning problems that require the use of QBF or other generalizations of SAT are discussed.

scholarly journals Improving AI Planning using Map Reduce

2020 ◽  
Vol 9 (4) ◽  
pp. 615-618
Keyword(s):  
Planning Process ◽  
Empirical Evaluation ◽  
Network Planning ◽  
Map Reduce ◽  
Ai Planning ◽  
Hierarchical Planning ◽  
Hierarchical Task Network ◽  
Problem Description ◽  
Hierarchical Task Network Planning

Today, the Landmark concept is adapted from the classical planning to work in hierarchical task network planning. It was shown how it is used to extracts landmark literals from a given hierarchical planning domain and problem description and then use these literals to update the the planning domain by ruling out the irrelevant tasks and methods before the actual planning is performed. In this paper, we compine the landmark concept with the Map-reduce framework to increase the performance of the planning process. Our empirical evaluation shows that the combination between landmark and Map-Reduce framework dramatically improves performance of the planning process.

scholarly journals Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System

2021 ◽  
Vol 13 (12) ◽  
pp. 6708
Author(s):  
Hamza Mubarak ◽  
Nurulafiqah Nadzirah Mansor ◽  
Hazlie Mokhlis ◽  
Mahazani Mohamad ◽  
Hasmaini Mohamad ◽  
...  
Keyword(s):  
Power Systems ◽  
Distribution System ◽  
Industrial Revolution ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Problem ◽  
System Expansion ◽  
Optimum Distribution ◽  
Optimal Sizing ◽  
Expansion Planning

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

scholarly journals What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

2018 ◽  
Vol 33 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Thomas L. Mccluskey
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Strong Impact ◽  
Significant Progress ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
The Future ◽  
International Planning ◽  
Planning Approaches

AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

scholarly journals Hybrid Technique for Data Scrutiny using Artificial Intelligence

2020 ◽  
Vol 8 (5) ◽  
pp. 4456-4459
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Uncertain Data ◽  
Genetic System ◽  
Search Space ◽  
Fuzzy Expert System ◽  
Hybrid Technique ◽  
Hybrid Techniques ◽  
Fuzzy Genetic

Today’s time is of artificial intelligence. We are trying to reduce the search space by some methods as uncertain data is also used in the system to solve problems. To manage our database we use hybrid techniques. Like Fuzzy expert system, fuzzy genetic system, in this paper we will see how can we use such system in solving an application.

Sign in / Sign up

Export Citation Format

Share Document