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.

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 The concept of operation and production control

2021 ◽  
Vol 27 (2) ◽  
pp. 100-107
Author(s):  
Radosław Wolniak
Keyword(s):  
Artificial Intelligence ◽  
Production Planning ◽  
Industry 4.0 ◽  
Production Management ◽  
Production Control ◽  
Main Function ◽  
Production Planning And Control ◽  
Factors Affecting ◽  
Planning And Control ◽  
And Control

Abstract The theoretical aim of the paper is to analyses the main function and concept of production control in operation management. The empirical aim of the paper is to investigate polish production firm opinion about factors affecting production planning and control and also functions of production planning and control. Production control is very important in every factory, and every aspect of operation and production management especially in times of Industry 4.0 conditions. In the paper we presented all classical seven task of production management control. Also there is in the paper an analysis of main factors affecting production control in industrial organization. In the paper we analysed the problems connected with production control. Nowadays in the conditions of Industry 4.0 this is very important concept because the increasing level of digitalization of all industrial processes leads to possibility of detailed analysis of all processes and better level of control. Operation managers should have good level of knowledge about production control and especially quality control. They can use in this many new information tools like statistical methods and artificial intelligence. Especially we think that in the future many function of production control would be assisted by artificial intelligence. We also in the paper give results of research conducted on example of 30 polish production organizations located in Silesia region.

scholarly journals Resilient Control and Safety for Multi-Agent Cyber-Physical Systems

2017 ◽  
Author(s):  
Anna Lukina
Keyword(s):  
Winning Strategy ◽  
Optimization Technique ◽  
Control Algorithms ◽  
Stochastic Dynamical Systems ◽  
Physical Systems ◽  
Planning And Control ◽  
Multi Agent ◽  
And Control ◽  
Non Cooperative Games ◽  
Synthesis Approach

I develop novel intelligent approximation algorithms for solving modern problems of CPSs, such as control and verification, by combining advanced statistical methods. it is important for the control algorithms underlying the class of multi-agent CPSs to be resilient to various kinds of attacks, and so it is for my algorithms. I have designed a very general adaptive receding-horizon synthesis approach to planning and control that can be applied to controllable stochastic dynamical systems. Apart from being fast and efficient, it provides statistical guarantees of convergence. The optimization technique based on the best features of Model Predictive Control and Particle Swarm Optimization proves to be robust in finding a winning strategy in the stochastic non-cooperative games against a malicious attacker. The technique can further benefit probabilistic model checkers and real-world CPSs.

scholarly journals System theoretic approach to sustainable development problems

2011 ◽  
Vol 21 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Vladan Batanovic ◽  
Slobodan Guberinic ◽  
Radivoj Petrovic
Keyword(s):  
Sustainable Development ◽  
State Space ◽  
Operations Research ◽  
The State ◽  
Feasible Region ◽  
Theoretic Approach ◽  
Initial State ◽  
The Sustainable Development ◽  
Planning And Control ◽  
And Control

This paper shows that the concepts and methodology contained in the system theory and operations research are suitable for application in the planning and control of the sustainable development. The sustainable development problems can be represented using the state space concepts, such as the transition of system, from the given initial state to the final state. It is shown that sustainable development represents a specific control problem. The peculiarity of the sustainable development is that the target is to keep the system in the prescribed feasible region of the state space. The analysis of planning and control problems of sustainable development has also shown that methods developed in the operations research area, such as multicriteria optimization, dynamic processes simulation, non-conventional treatment of uncertainty etc. are adequate, exact base, suitable for resolution of these problems.

scholarly journals Material requirement planning in a rubber micro-operation

2020 ◽  
Vol 9 (8) ◽  
pp. e703985285
Author(s):  
Maximiliano dos Santos Alves ◽  
Douglas Vieira Barboza ◽  
Ricardo Luiz Fernandes Bella ◽  
Wellington Rodrigues Silva ◽  
Marcelo Jasmim Meiriño
Keyword(s):  
Production Control ◽  
Production Planning And Control ◽  
Specific Context ◽  
Diffusion Of Technology ◽  
Planning And Control ◽  
Processing Times ◽  
Material Requirement ◽  
Current Production ◽  
Control Knowledge ◽  
And Control

The economic and social development of a nation depends, among other variables, on the diffusion of technology from macro levels to micro operations levels. The aim of this paper is to illustrate the application of production planning and control knowledge in a micro operation of rubber artifacts. For this, the current production process was analyzed through internal records and interviews with company managers in order to propose a better organization of the items needed for production and their processing times. The main contribution of this work is the application of the material planning logic in a specific context, as well as the exercise of outlining important parameters for production control.

Artificial Intelligence in Manufacturing Planning and Control

1980 ◽  
Vol 12 (4) ◽  
pp. 351-363 ◽  
Author(s):  
William I. Bullers ◽  
Shimon Y. Nof ◽  
Andrew B. Whinston
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Planning ◽  
Planning And Control ◽  
Manufacturing Planning And Control ◽  
And Control
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