Scheduling And Htn
•
1 like•475 views
The document discusses hierarchical task network (HTN) planning and its advantages over STRIPS and partial-order planning for modeling complex real-world planning domains. It describes HTN as decomposing high-level goals into primitive plans through abstract and non-primitive operators. An example HTN operator for construction is provided. The SIPE-2 planner, which uses HTN planning, is also summarized.
Report
Share
Report
Share
![Outline ,[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Artificial Intelligence 1 Planning In The Real World
The document discusses planning for problems with time, resources, and uncertainty. It covers job shop scheduling, hierarchical task network planning, conditional planning for non-deterministic domains, and techniques for monitoring plan execution and replanning if needed to handle unexpected outcomes or state changes. Key concepts include temporal planning with durations and timelines, resource constraints, hierarchical decomposition, conditional branches, belief states for partial observability, and replanning to repair or replace plans that fail during execution.
Planning Algorithms
The document describes an exam format for a planning algorithms course. The exam will consist of 4 questions, all of which must be completed. Question 1 involves discussing different planning approaches and their advantages/disadvantages. Questions 2-3 require applying planning algorithms to sample problems. Question 4 involves short answers about specific planning algorithms. The document also lists several planning algorithms that students should know, including STRIPS, Graphplan, and HTNs.
Planing presentation
This document discusses various topics in artificial intelligence planning, including:
1. Planning involves automatically generating action strategies (plans) from descriptions of actions, sensors, and goals.
2. Key elements of planning include representation languages, mathematical models, and algorithms for solving planning problems effectively.
3. Classical planning assumes deterministic state models, while planning with uncertainty involves non-deterministic or probabilistic state transitions. Planning with sensing adds the ability to gather observations.
Cs221 lecture7-fall11
This document summarizes key concepts in artificial intelligence planning and logic. It discusses representations like atomic, factored, and structured states. Planning approaches include state-space search, planning graphs, and situation calculus. Factored representations allow more flexible and hierarchical plans using relations between state variables. Planning graphs efficiently represent possible plan states and actions to derive heuristic estimates and extract plans.
Intrinsically Motivated Reinforcement Learning
The document discusses intrinsically motivated reinforcement learning. It presents a graphical model showing how an agent learns a policy by optimizing both expected extrinsic reward (value) and intrinsic information gain together using Bellman-like equations. The agent interacts with an environment over discrete time steps, taking actions and receiving rewards while also estimating internal states and information gain. The value and information equations are combined using a temperature parameter to optimize both goals of minimizing decision complexity and maximizing environmental information gain. Examples on a grid world demonstrate how the agent's behavior changes with different temperature parameters when trading off value and information.
AI: Planning and AI
A situated planning agent treats planning and acting as a single process rather than separate processes. It uses conditional planning to construct plans that account for possible contingencies by including sensing actions. The agent resolves any flaws in the conditional plan before executing actions when their conditions are met. When facing uncertainty, the agent must have preferences between outcomes to make decisions using utility theory and represent probabilities using a joint probability distribution over variables in the domain.
Optimizing product development projects under asynchronous and aperiodic syst...
M. Ogura, M. Kishida, and A. Yassine, “Optimizing product development projects under asynchronous and aperiodic system-local interactions,” in 21st International DSM Conference, 2019, pp. 97-106.
Deep Learning and Optimization Methods
Talk at the Data Science Meetup Hamburg about Deep Learning, the most important Optimization methods in this field and the relationship between training and learning
Recommended
Artificial Intelligence 1 Planning In The Real World
The document discusses planning for problems with time, resources, and uncertainty. It covers job shop scheduling, hierarchical task network planning, conditional planning for non-deterministic domains, and techniques for monitoring plan execution and replanning if needed to handle unexpected outcomes or state changes. Key concepts include temporal planning with durations and timelines, resource constraints, hierarchical decomposition, conditional branches, belief states for partial observability, and replanning to repair or replace plans that fail during execution.
Planning Algorithms
The document describes an exam format for a planning algorithms course. The exam will consist of 4 questions, all of which must be completed. Question 1 involves discussing different planning approaches and their advantages/disadvantages. Questions 2-3 require applying planning algorithms to sample problems. Question 4 involves short answers about specific planning algorithms. The document also lists several planning algorithms that students should know, including STRIPS, Graphplan, and HTNs.
Planing presentation
This document discusses various topics in artificial intelligence planning, including:
1. Planning involves automatically generating action strategies (plans) from descriptions of actions, sensors, and goals.
2. Key elements of planning include representation languages, mathematical models, and algorithms for solving planning problems effectively.
3. Classical planning assumes deterministic state models, while planning with uncertainty involves non-deterministic or probabilistic state transitions. Planning with sensing adds the ability to gather observations.
Cs221 lecture7-fall11
This document summarizes key concepts in artificial intelligence planning and logic. It discusses representations like atomic, factored, and structured states. Planning approaches include state-space search, planning graphs, and situation calculus. Factored representations allow more flexible and hierarchical plans using relations between state variables. Planning graphs efficiently represent possible plan states and actions to derive heuristic estimates and extract plans.
Intrinsically Motivated Reinforcement Learning
The document discusses intrinsically motivated reinforcement learning. It presents a graphical model showing how an agent learns a policy by optimizing both expected extrinsic reward (value) and intrinsic information gain together using Bellman-like equations. The agent interacts with an environment over discrete time steps, taking actions and receiving rewards while also estimating internal states and information gain. The value and information equations are combined using a temperature parameter to optimize both goals of minimizing decision complexity and maximizing environmental information gain. Examples on a grid world demonstrate how the agent's behavior changes with different temperature parameters when trading off value and information.
AI: Planning and AI
A situated planning agent treats planning and acting as a single process rather than separate processes. It uses conditional planning to construct plans that account for possible contingencies by including sensing actions. The agent resolves any flaws in the conditional plan before executing actions when their conditions are met. When facing uncertainty, the agent must have preferences between outcomes to make decisions using utility theory and represent probabilities using a joint probability distribution over variables in the domain.
Optimizing product development projects under asynchronous and aperiodic syst...
M. Ogura, M. Kishida, and A. Yassine, “Optimizing product development projects under asynchronous and aperiodic system-local interactions,” in 21st International DSM Conference, 2019, pp. 97-106.
Deep Learning and Optimization Methods
Talk at the Data Science Meetup Hamburg about Deep Learning, the most important Optimization methods in this field and the relationship between training and learning
Ai planning with evolutionary computing
AI planning with Evolutionary Computing, created for the AI Planning course of the university of Edinburgh, given on coursera
#NoEstimates project planning using Monte Carlo simulation
Here is the text behind the slides http://www.infoq.com/articles/noestimates-monte-carlo
Here is a video I prepared in order to help people understand how to plan a release using the Monte Carlo simulation in MS Excel http://youtu.be/r38a25ak4co
And here is an Excel file to show how Monte Carlo is done http://modernmanagement.bg/data/NoEstimate_Project_Planning_MonteCarlo.xlsx
Here are the SIPs for the baseline project http://modernmanagement.bg/data/SIPs_MonteCarlo_FVR.xlsx
Here is the planing simulation in Excel http://modernmanagement.bg/data/High_Level_Project_Planning.xlsx
The video ( after the 3:00 minute) http://youtu.be/GE9vrJ741WY on how to use the Excel files
AI: Logic in AI 2
A simple planning agent uses percepts from the environment to build a model of the current state and calls a planning algorithm to generate a plan to achieve its goal. Practical planning involves restricting the language used to define problems, using specialized planners rather than general theorem provers, and adopting hierarchical decomposition to store and retrieve abstract plans from a library. A solution is a fully instantiated, totally ordered plan that guarantees achieving the goal.
Efficiency gains in inversion based interpretation through computer
By leveraging self-organized maps and seismic inversion products, it is possible to:
1) More rapidly localize interpretation in a dataset while utilizing specialized geological knowledge.
2) Potentially make substantial gains in both efficiency and interpretation quality.
3) Increase the engagement of non-specialists.
The method classifies inversion results with an unbiased algorithm to find anomalous areas for specialists to focus on, helping maximize their time. However, specialists should still understand the physics behind any anomalies found.
PERT
The document discusses Program Evaluation and Review Technique (PERT), which is a project management tool used to schedule, organize, and coordinate tasks within a project. PERT involves analyzing project tasks, estimating the time needed for each, and identifying the minimum time to complete the project. It is used when activity times are uncertain. PERT determines the project duration by calculating expected times using optimistic, realistic, and pessimistic estimates for each task. A network diagram is constructed to show task sequences and relationships.
160615 Progress Update WP3 (Nils Knofius)
This document summarizes experiments on the costs of consolidating parts using additive manufacturing. Experiment 1 found that consolidation was only marginally beneficial in a few cases when parts had different parameters. Experiment 2 found consolidation was always better when parts were in an assembly, even if parameters worsened. Experiment 3 found partial consolidation on one hierarchy level was beneficial in 39% of cases. In conclusion, consolidation is more desirable between parts with similar parameters and when it eliminates hierarchy in the assembly. Future work is outlined to model more complex cases.
Value Function Approximation via Low-Rank Models
We propose a novel value function approximation technique for Markov decision processes that compactly represents the state-action value function using a low-rank and sparse matrix model. Under minimal assumptions, this decomposition is a Robust Principal Component Analysis problem that can be solved exactly via the Principal Component Pursuit convex optimization problem.
2.6.2 graphing techniques
This document discusses graphing techniques for transforming functions including the order of transformations such as reflection, stretch/compression, horizontal translation, and vertical translation. It also lists homework problems from pages 128 and 134 that involve applying these graphing techniques to functions.
RCIM 2008 - Modello Scheduling
The document summarizes research on task scheduling techniques for dynamically reconfigurable systems. It presents (1) an integer linear programming model to formally define the scheduling problem, (2) the Napoleon heuristic scheduler to solve the problem in reasonable time based on the ILP model, and (3) experimental results validating that Napoleon obtains an average 18.6% better schedule length than other algorithms. Future work is outlined to integrate Napoleon into a general design framework and scheduling-aware partitioning flow.
Graphical RepresentationLinear programming
Graphical Representation is one of a Tool of linear Programming.
After going through the slides, it helps you to clarify your doubt on LLP.
POMDP Seminar Backup3
This document summarizes an article about planning and acting in partially observable stochastic domains. It introduces the concepts of partially observable Markov decision processes (POMDPs) which involve uncertainty about the system state. Key points covered include:
- POMDPs extend Markov decision processes to include uncertainty about the true state due to partial observability
- The agent's uncertainty can be represented by a "belief state" which is a probability distribution over possible world states
- Finding optimal policies in POMDPs involves reasoning about belief state transitions in response to actions and observations
- Exact solutions can be computed using a witness algorithm that iteratively constructs and prunes policy trees representing the value function
Lecture 07 search techniques
This document discusses various search techniques used in artificial intelligence problem solving. It defines a search problem as consisting of an initial state, goal state, set of all possible states, and operators to transform between states. Uninformed searches like breadth-first search explore the state space without any heuristics, while informed searches use heuristics to guide the search. The performance of different search strategies is evaluated based on completeness, optimality, time complexity and space complexity.
Dynamic programming 2
Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable when problems exhibit overlapping subproblems that are only slightly smaller. The method involves 4 steps: 1) developing a mathematical notation to express solutions, 2) proving the principle of optimality holds, 3) deriving a recurrence relation relating solutions to subsolutions, and 4) writing an algorithm to compute the recurrence relation. Dynamic programming yields optimal solutions when the principle of optimality holds, without needing to prove optimality. It is used to solve production, scheduling, resource allocation, and inventory problems.
Finding Robust Solutions to Requirements Models
The document presents research on finding robust solutions to requirements models. It introduces the KEYS2 algorithm, which incrementally sets mitigation variables to balance solution quality and robustness. KEYS2 is faster than other algorithms like simulated annealing and A* search. It generates well-behaved results that allow exploring the decision neighborhood through decision ordering diagrams to assess robustness. The researcher concludes that KEYS2 is well-suited for optimizing requirements models as it is fast, produces stable solutions, and enables robustness analysis.
Size measurement and estimation
It is rather surprising that in software engineering, standard measurement units have yet to be
widely accepted and used. Every other engineering discipline has their own. By and large, effort
is the most commonly used parameter for measuring software initiatives. The problem of
course is that effort is not an independent variable – it depends on who is doing the work and
how it is done. This presentation looks at an approach that has been used to convert the large
amount of effort data usually collected in an organization into something that can meaningfully
be used for estimation and comparison purposes.
Cs221 logic-planning
This document summarizes an introduction to artificial intelligence planning and logic. It discusses different types of planning problems and representations including classical planning with STRIPS, planning with factored states, partial observability, and extensions like planning graphs and situation calculus. The document also provides an overview of the GRAPHPLAN algorithm for solving planning problems using planning graph representations.
RPT_AI-06_A_Planning Intro.ppt
The document discusses planning and problem solving techniques in artificial intelligence. It covers classical planning problems, planning languages like STRIPS and PDDL, and different planning approaches like state-space planning using progression and regression, and partial-order planning. State-space planners search through the space of world states by applying actions, while partial-order planners delay commitments and search through the space of partially ordered plans. Representing problems in a logical planning language allows more powerful techniques compared to general problem solving.
Robust Agent Execution
The document discusses an approach called the Integrated Influence Architecture for planning agent execution in dynamic environments. It generates "influence landscapes" from multiple data sources to allow agents to react deliberatively or deliberate reactively based on a continuous range of stimuli. Key components include stacks that produce landscapes from domain structure, environmental data, and plan data. The unified landscape guides agent decisions while allowing loose conformity to plans for flexibility.
AI: Logic in AI 2
A simple planning agent uses percepts from the environment to build a model of the current state and calls a planning algorithm to generate a plan to achieve its goal. Practical planning involves restricting the language used to define problems, using specialized planners rather than general theorem provers, and adopting hierarchical decomposition to store and retrieve abstract plans from a library. A solution is a fully instantiated, totally ordered plan that guarantees achieving the goal.
Lesson 22
The document discusses planning and logic-based planning. It covers:
1. The formulation of planning problems and differences between planning and problem solving.
2. Representing planning problems using STRIPS and situation calculus.
3. Planning algorithms like situation space planning and plan space planning.
4. Issues with full commitment planning and the need for least commitment planning.
The goal is to teach students how to represent planning problems logically and solve them using suitable planning algorithms.
AI Lesson 22
This document discusses planning and logic-based planning. It provides an overview of planning problems and algorithms. The key points are:
1. Planning involves finding a sequence of actions to achieve a goal starting from an initial state. Planning can be viewed as a type of problem solving that uses knowledge about actions and their consequences.
2. Logic-based planning represents planning problems using formal logics like situation calculus. Situation calculus extends first-order logic to reason about how the world changes over time as actions are performed.
3. The document discusses the differences between problem solving and planning approaches. Planning emphasizes using representations of states, goals, and operators to intelligently select actions, rather than incremental problem solving from
An Ontological Formalization Of The Planning Task
The document presents an ontological formalization of planning tasks. It aims to provide a precise and comprehensive model of planning problems independently of any application or method. The approach uses ontologies and the task-method-domain-application framework to analyze planning concepts, relations, and axioms. Key aspects of planning problems like initial states, goals, tasks, actions, agents, time, constraints, and preferences are formally defined. The planning task ontology developed takes a fine-grained, generic view of the planning problem space.
More Related Content
What's hot
Ai planning with evolutionary computing
AI planning with Evolutionary Computing, created for the AI Planning course of the university of Edinburgh, given on coursera
#NoEstimates project planning using Monte Carlo simulation
Here is the text behind the slides http://www.infoq.com/articles/noestimates-monte-carlo
Here is a video I prepared in order to help people understand how to plan a release using the Monte Carlo simulation in MS Excel http://youtu.be/r38a25ak4co
And here is an Excel file to show how Monte Carlo is done http://modernmanagement.bg/data/NoEstimate_Project_Planning_MonteCarlo.xlsx
Here are the SIPs for the baseline project http://modernmanagement.bg/data/SIPs_MonteCarlo_FVR.xlsx
Here is the planing simulation in Excel http://modernmanagement.bg/data/High_Level_Project_Planning.xlsx
The video ( after the 3:00 minute) http://youtu.be/GE9vrJ741WY on how to use the Excel files
AI: Logic in AI 2
A simple planning agent uses percepts from the environment to build a model of the current state and calls a planning algorithm to generate a plan to achieve its goal. Practical planning involves restricting the language used to define problems, using specialized planners rather than general theorem provers, and adopting hierarchical decomposition to store and retrieve abstract plans from a library. A solution is a fully instantiated, totally ordered plan that guarantees achieving the goal.
Efficiency gains in inversion based interpretation through computer
By leveraging self-organized maps and seismic inversion products, it is possible to:
1) More rapidly localize interpretation in a dataset while utilizing specialized geological knowledge.
2) Potentially make substantial gains in both efficiency and interpretation quality.
3) Increase the engagement of non-specialists.
The method classifies inversion results with an unbiased algorithm to find anomalous areas for specialists to focus on, helping maximize their time. However, specialists should still understand the physics behind any anomalies found.
PERT
The document discusses Program Evaluation and Review Technique (PERT), which is a project management tool used to schedule, organize, and coordinate tasks within a project. PERT involves analyzing project tasks, estimating the time needed for each, and identifying the minimum time to complete the project. It is used when activity times are uncertain. PERT determines the project duration by calculating expected times using optimistic, realistic, and pessimistic estimates for each task. A network diagram is constructed to show task sequences and relationships.
160615 Progress Update WP3 (Nils Knofius)
This document summarizes experiments on the costs of consolidating parts using additive manufacturing. Experiment 1 found that consolidation was only marginally beneficial in a few cases when parts had different parameters. Experiment 2 found consolidation was always better when parts were in an assembly, even if parameters worsened. Experiment 3 found partial consolidation on one hierarchy level was beneficial in 39% of cases. In conclusion, consolidation is more desirable between parts with similar parameters and when it eliminates hierarchy in the assembly. Future work is outlined to model more complex cases.
Value Function Approximation via Low-Rank Models
We propose a novel value function approximation technique for Markov decision processes that compactly represents the state-action value function using a low-rank and sparse matrix model. Under minimal assumptions, this decomposition is a Robust Principal Component Analysis problem that can be solved exactly via the Principal Component Pursuit convex optimization problem.
2.6.2 graphing techniques
This document discusses graphing techniques for transforming functions including the order of transformations such as reflection, stretch/compression, horizontal translation, and vertical translation. It also lists homework problems from pages 128 and 134 that involve applying these graphing techniques to functions.
RCIM 2008 - Modello Scheduling
The document summarizes research on task scheduling techniques for dynamically reconfigurable systems. It presents (1) an integer linear programming model to formally define the scheduling problem, (2) the Napoleon heuristic scheduler to solve the problem in reasonable time based on the ILP model, and (3) experimental results validating that Napoleon obtains an average 18.6% better schedule length than other algorithms. Future work is outlined to integrate Napoleon into a general design framework and scheduling-aware partitioning flow.
Graphical RepresentationLinear programming
Graphical Representation is one of a Tool of linear Programming.
After going through the slides, it helps you to clarify your doubt on LLP.
POMDP Seminar Backup3
This document summarizes an article about planning and acting in partially observable stochastic domains. It introduces the concepts of partially observable Markov decision processes (POMDPs) which involve uncertainty about the system state. Key points covered include:
- POMDPs extend Markov decision processes to include uncertainty about the true state due to partial observability
- The agent's uncertainty can be represented by a "belief state" which is a probability distribution over possible world states
- Finding optimal policies in POMDPs involves reasoning about belief state transitions in response to actions and observations
- Exact solutions can be computed using a witness algorithm that iteratively constructs and prunes policy trees representing the value function
Lecture 07 search techniques
This document discusses various search techniques used in artificial intelligence problem solving. It defines a search problem as consisting of an initial state, goal state, set of all possible states, and operators to transform between states. Uninformed searches like breadth-first search explore the state space without any heuristics, while informed searches use heuristics to guide the search. The performance of different search strategies is evaluated based on completeness, optimality, time complexity and space complexity.
Dynamic programming 2
Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable when problems exhibit overlapping subproblems that are only slightly smaller. The method involves 4 steps: 1) developing a mathematical notation to express solutions, 2) proving the principle of optimality holds, 3) deriving a recurrence relation relating solutions to subsolutions, and 4) writing an algorithm to compute the recurrence relation. Dynamic programming yields optimal solutions when the principle of optimality holds, without needing to prove optimality. It is used to solve production, scheduling, resource allocation, and inventory problems.
Finding Robust Solutions to Requirements Models
The document presents research on finding robust solutions to requirements models. It introduces the KEYS2 algorithm, which incrementally sets mitigation variables to balance solution quality and robustness. KEYS2 is faster than other algorithms like simulated annealing and A* search. It generates well-behaved results that allow exploring the decision neighborhood through decision ordering diagrams to assess robustness. The researcher concludes that KEYS2 is well-suited for optimizing requirements models as it is fast, produces stable solutions, and enables robustness analysis.
Size measurement and estimation
It is rather surprising that in software engineering, standard measurement units have yet to be
widely accepted and used. Every other engineering discipline has their own. By and large, effort
is the most commonly used parameter for measuring software initiatives. The problem of
course is that effort is not an independent variable – it depends on who is doing the work and
how it is done. This presentation looks at an approach that has been used to convert the large
amount of effort data usually collected in an organization into something that can meaningfully
be used for estimation and comparison purposes.
What's hot (15)
#NoEstimates project planning using Monte Carlo simulation
#NoEstimates project planning using Monte Carlo simulation
Efficiency gains in inversion based interpretation through computer
Efficiency gains in inversion based interpretation through computer
Similar to Scheduling And Htn
Cs221 logic-planning
This document summarizes an introduction to artificial intelligence planning and logic. It discusses different types of planning problems and representations including classical planning with STRIPS, planning with factored states, partial observability, and extensions like planning graphs and situation calculus. The document also provides an overview of the GRAPHPLAN algorithm for solving planning problems using planning graph representations.
RPT_AI-06_A_Planning Intro.ppt
The document discusses planning and problem solving techniques in artificial intelligence. It covers classical planning problems, planning languages like STRIPS and PDDL, and different planning approaches like state-space planning using progression and regression, and partial-order planning. State-space planners search through the space of world states by applying actions, while partial-order planners delay commitments and search through the space of partially ordered plans. Representing problems in a logical planning language allows more powerful techniques compared to general problem solving.
Robust Agent Execution
The document discusses an approach called the Integrated Influence Architecture for planning agent execution in dynamic environments. It generates "influence landscapes" from multiple data sources to allow agents to react deliberatively or deliberate reactively based on a continuous range of stimuli. Key components include stacks that produce landscapes from domain structure, environmental data, and plan data. The unified landscape guides agent decisions while allowing loose conformity to plans for flexibility.
AI: Logic in AI 2
A simple planning agent uses percepts from the environment to build a model of the current state and calls a planning algorithm to generate a plan to achieve its goal. Practical planning involves restricting the language used to define problems, using specialized planners rather than general theorem provers, and adopting hierarchical decomposition to store and retrieve abstract plans from a library. A solution is a fully instantiated, totally ordered plan that guarantees achieving the goal.
Lesson 22
The document discusses planning and logic-based planning. It covers:
1. The formulation of planning problems and differences between planning and problem solving.
2. Representing planning problems using STRIPS and situation calculus.
3. Planning algorithms like situation space planning and plan space planning.
4. Issues with full commitment planning and the need for least commitment planning.
The goal is to teach students how to represent planning problems logically and solve them using suitable planning algorithms.
AI Lesson 22
This document discusses planning and logic-based planning. It provides an overview of planning problems and algorithms. The key points are:
1. Planning involves finding a sequence of actions to achieve a goal starting from an initial state. Planning can be viewed as a type of problem solving that uses knowledge about actions and their consequences.
2. Logic-based planning represents planning problems using formal logics like situation calculus. Situation calculus extends first-order logic to reason about how the world changes over time as actions are performed.
3. The document discusses the differences between problem solving and planning approaches. Planning emphasizes using representations of states, goals, and operators to intelligently select actions, rather than incremental problem solving from
An Ontological Formalization Of The Planning Task
The document presents an ontological formalization of planning tasks. It aims to provide a precise and comprehensive model of planning problems independently of any application or method. The approach uses ontologies and the task-method-domain-application framework to analyze planning concepts, relations, and axioms. Key aspects of planning problems like initial states, goals, tasks, actions, agents, time, constraints, and preferences are formally defined. The planning task ontology developed takes a fine-grained, generic view of the planning problem space.
CCP_SEC3_Planning and Scheduling
A CCP is an experienced practitioner with advanced knowledge and technical expertise to apply the broad principles and best practices of Total Cost Management (TCM) in the planning, execution and management of any organizational project or program. CCPs also demonstrate the ability to research and communicate aspects of TCM principles and practices to all levels of project or program stakeholders, both internally and externally.
AI_Planning.pdf
The document discusses various approaches to artificial intelligence planning, including state-space planning and plan-space planning. State-space planning formulates finding a plan as a search through the space of possible states, using either forward progression search or backward regression search. Plan-space planning formulates finding a plan as a search through the space of possible partial plans, starting with an initial partial plan and iteratively improving it. Specific planning techniques discussed include STRIPS planning, which represents states and actions using logical predicates, and progression and regression planners, which use forward and backward search respectively through the state space.
AI_unit IV Full Notes.pdf
Planning involves generating a sequence of actions to achieve a goal. Classical planning represents states as conjunctions of literals and actions as operators with preconditions and effects. Planning algorithms use state space search to find a plan by searching forward from the initial state or backward from the goal state. Heuristics like relaxed planning can guide the search. Partial order planning allows actions to be unordered if their preconditions and effects do not conflict.
Concepts of predictive control
Introductory course on concepts used in predictive control. For more files and MATLAB suporting information go to:
http://controleducation.group.shef.ac.uk/OER_index.htm
Planning Agent
Planning involves representing an initial state, possible actions, and a goal state. A planning agent uses a knowledge base to select action sequences that transform the initial state into a goal state. STRIPS is a common planning representation that uses predicates to describe states and logical operators to represent actions and their effects. A STRIPS planning problem specifies the initial state, goal conditions, and set of operators. A solution is a sequence of ground operator instances that produces the goal state from the initial state.
Unit 5 Introduction to Planning and ANN.pptx
This document provides information about an artificial intelligence course at Sanjivani College of Engineering, including the course objectives, an overview of planning in AI, types of planning strategies like forward and backward state space planning, and an example of the block world planning problem. It also discusses logical representations of planning problems using first-order logic, the STRIPS planning framework, and an algorithm for goal stack planning.
Slides chapters 24-25
The document discusses project scheduling and tracking in software engineering. It provides reasons why projects may be late, such as unrealistic deadlines or changing requirements. It discusses principles for effective scheduling like compartmentalization of tasks and defining responsibilities. Metrics like earned value analysis are presented to quantitatively track project progress versus what was planned. Risk management techniques like proactive risk analysis are outlined to improve project success.
Generalized Analysis Value Behavior
Generalized Analysis of Value Behavior over Time
as a
Project Performance Predictor
As projects have grown more complex our performance analysis frameworks have
remained largely unchanged even as newer more powerful tools have become available
to manage and manipulate large volumes of data. Newer analytical tools provide deeper
insights into existing data sets especially from a statistical point of view but we continue
to use traditional project metrics to assess project performance on both a retrospective
as well as prospective basis.
DMAIC
This document discusses various statistical methods used in engineering. It covers topics like sample plans, capability studies, gauge R&R studies, comparative analysis, design of experiments (DOE), correlation, regression, reliability, and the DMAIC process in Six Sigma. DOE techniques like full factorial designs, fractional factorial designs, custom designs, evaluation of designs, response surface methods, and residuals are explained. The document provides examples and outlines the applications of these various statistical analysis methods.
SE chapters 24-25
This document discusses risks that can lead to software project delays or failures. It identifies several categories of risk, including risks due to product size, business impact, customers, process maturity, technology, staff size and experience. It also discusses approaches for identifying, assessing, prioritizing and mitigating risks, including building a risk table to estimate probability and impact of each risk. Project managers are advised to take a proactive approach to risk management to avoid reactive crisis management when risks occur.
Operations and Supply Chain ManagementMGMT 3306Lecture 02I.docx
Operations and Supply Chain Management
MGMT 3306
Lecture 02
Instructor: Dr. Yan Qin
Outline
Project Management
What is Project Management?
Three types of projects
Work Breakdown Structure (WBS)
Project Management Techniques: PERT and CPM
Cost-Time Trade-Offs and Project Crashing
A Critique of PERT and CPM
2
Project Management
A project is a series of related jobs usually directed toward some major output and requiring a significant period of time to perform.
Project Management is the planning, scheduling, and controlling of resources to meet the technical, cost, and time constraints of the project.
3
Examples of Projects
Building Construction
Research Project
Three Phases of Project Management
The management of projects involves three phases:
Planning - goal setting, defining the project, team organization
Scheduling - relates people, money, and supplies to specific activities and activities to each other
Controlling - monitors resources, costs, quality, and budgets; revises plans and shifts resources to meet time and cost demands
Project Planning
Figure 3.1
BeforeStart of projectDuring
projectTimelineproject
Project Scheduling
Figure 3.1
BeforeStart of projectDuring
projectTimelineproject
Project Controlling
Figure 3.1
BeforeStart of projectDuring
projectTimelineproject
Project Planning, Scheduling, and Controlling
Figure 3.1
BeforeStart of projectDuring
projectTimelineproject
Project Organizations
There are three types of project based on the organizational structures used to tie the project to the parent firm:
Pure project: A self-contained team works full time on a project;
Functional project: A project housed within a functional division.
Matrix project: A project that uses people from different functional areas.
The project manager decides what tasks to complete and when;
The functional managers control which people and technologies to be used in the project.
10
Example of Matrix Project
MarketingOperationsEngineeringFinance
Project 1
Project 2
Project 3
Project 4
Work Breakdown Structure (WBS)
Work Breakdown Structure (WBS): defines the hierarchy of project tasks, subtasks, and work packages
Program
Project 1
Project 2
Task 1.1
Subtask 1.1.1
Work Package 1.1.1.1
Level
1
2
3
4
Task 1.2
Subtask 1.1.2
Work Package 1.1.1.2
Task is a further subdivision of a project, usually shorter than several months.
Work package is a group of activities combined to be assignable to a single organizational unit, usually an individual.
12
Example: WBS – Large Optical Scanner Design
13
Purposes of Project Scheduling
Shows the relationship of each activity to others and to the whole project
Identifies the precedence relationships among activities
Encourages the setting of realistic time and cost estimates for each activity
Helps make better use of people, money, and material resources .
Introduction to schedule management
The document provides details on project schedule management including planning the schedule, defining activities, estimating activity durations, developing the schedule, and performing schedule network analysis. It discusses scheduling tools and techniques such as the critical path method, resource optimization, precedence diagramming, and developing duration estimates using methods like analogous estimates, parametric estimates, and three-point estimates. The document also includes examples of a precedence diagram network and critical path pointers.
Introduction Construction Planning and Management
This document provides an overview of project planning, management, and scheduling techniques. It discusses the characteristics of projects and objectives of project management such as completing projects on time and within budget. Project management involves planning, scheduling, and controlling a project through completion. Planning defines the goals and strategies. Scheduling allocates resources and assigns start/end dates to activities. Controlling monitors schedule and budget variances and identifies corrective actions. Network methods like CPM and PERT use activity-oriented or event-oriented diagrams to visually represent project schedules and dependencies. CPM is suited for construction while PERT is for research projects with uncertain durations.
Similar to Scheduling And Htn (20)
Operations and Supply Chain ManagementMGMT 3306Lecture 02I.docx
Operations and Supply Chain ManagementMGMT 3306Lecture 02I.docx
More from ahmad bassiouny
Work Study & Productivity
This document discusses work study techniques used to analyze human work and improve efficiency. It defines work study, method study, and work measurement. Method study examines work processes to develop more effective methods using a six-step approach. Work measurement techniques include time study, activity sampling, and predetermined time systems to establish standard times for tasks. The goals of work study are to optimize the use of resources, increase productivity, reduce costs, and improve working conditions and quality.
Work Study
Work study is a technique used to systematically analyze work processes to improve efficiency. It involves observing work, documenting the current process, analyzing it for improvements, developing a more efficient method, measuring the new method, and implementing it as the standard. The key founders who developed techniques in this area include Frederick Taylor with time motion studies, Frank and Lillian Gilbreth with motion studies, and Henry Gantt with task scheduling. Work study specialists must gain cooperation from supervisors and workers, and consider the human factors, to successfully conduct a study and implement new standards.
Motion And Time Study
Motion and Time Study are methods used to analyze work processes and determine standard times. Frank and Lillian Gilbreth pioneered Motion Study in the 1880s to analyze body motions. Frederick Taylor developed Time Study in the 1880s to measure task completion times. Modern tools like motion cameras, stopwatches, and software are used to study processes in manufacturing, offices, hospitals and more in order to identify inefficiencies and establish performance standards.
Motion Study
The document discusses principles and approaches for improving work efficiency through motion study and work design, including:
1. Four principles of motion economy to reduce unnecessary movement - reduce motions, perform simultaneously, shorten distances, make motions easier.
2. Ergonomic considerations for tool and workstation layout such as positioning parts and tools for sequential use, gravity feeding, and correct working heights.
3. Methods for analyzing and improving operations including eliminating unnecessary steps, simplifying processes, combining tasks, and rearranging sequences.
The Christmas Story
The passage describes the birth of Jesus Christ as foretold by prophets. An angel tells Mary she will conceive and give birth to Jesus, the Son of God. Mary visits her relative Elizabeth, who is also miraculously pregnant. Mary gives birth to Jesus in Bethlehem, where he is visited by shepherds and wise men. The passage establishes Jesus as the promised Messiah through fulfillment of prophecies.
Turkey Photos
The document provides a list of images related to sights and landmarks in Turkey. It includes pictures of mountains, cave houses, mosques, palaces, bridges, museums, universities, and natural areas from various cities across Turkey including Istanbul, Ankara, Konya, Antalya, and Edirne. The images showcase Turkey's rich cultural and architectural heritage as well as natural beauty.
Mission Bo Kv3
The Society of Concurrent Product Development (SOCPD) aims to promote integrated product development practices. Its mission is to disseminate knowledge of concurrent engineering and further develop its body of knowledge. The document outlines SOCPD's history and evolution since the 1980s, including expanding its mission, objectives, and body of knowledge to keep up with emerging techniques and technologies in concurrent product development.
Miramar
The document discusses Miramar Automation's industrial automation and control engineering services. It lists hundreds of successful projects in areas like process control, SCADA, telemetry, water handling, and beverage preparation. It also outlines the company's capabilities such as software development, panel/PLC integration, gas detection systems, engineering documentation, and training.
Mom
The document is a poem about a mother's unconditional love and sacrifice for her child from birth through adulthood. It describes how the mother was always there to support and celebrate her child at each milestone, from standing up and taking first steps to starting school, getting sick, riding a bicycle, and becoming independent. It reminds the reader to show their love and spend time with their mother while they can, as the mother has given her all through the years and now in old age just desires a bit of her child's love and time in return.
Linearization
Linearization involves developing a linear approximation of a nonlinear system around an operating point. This allows tools from linear systems theory to be applied to analyze and design controllers for nonlinear systems. Specifically, Taylor's theorem is used to expand the nonlinear functions as a linear combination of deviations from the operating point. The resulting linearized model is only valid locally but provides an approximate way to analyze system behavior if well-controlled near the operating point. Examples show how to derive linearized models for common nonlinear systems like tanks and chemical reactors.
Kblmt B000 Intro Kaizen Based Lean Manufacturing
The document introduces Kaizen-based Lean Manufacturing (KBLM) as a way for businesses to increase speed, improve quality, and lower costs compared to traditional Material Requirements Planning (MRP) systems. It discusses eight basics of KBLM, including information integrity, motivational measurement, sequential production, point-of-use logistics, cycle time management, production linearity, resource planning, and customer connectivity. Case studies show companies achieving reductions in costs, inventory levels, and product build times by implementing KBLM.
How To Survive
The document provides seven rules for surviving in a new economy: 1) Accept ambiguity and uncertainty; 2) Hold yourself accountable for outcomes; 3) Become a quick change artist and adapt to changes; 4) Add value through your work; 5) Be a problem solver rather than pointing fingers; 6) Practice continuous improvement through lean processes; 7) Continue learning new skills. The introductory quote emphasizes embracing positive change and progress rather than resisting it or becoming part of negative forces that stand in the way.
Dad
A son or daughter's perspective of their father changes dramatically over time, from seeing their father as all-knowing when young, to viewing them as outdated and unreasonable during the teenage years, to later gaining appreciation for all the hardships and sacrifices their father endured to raise them as they become parents themselves.
Ancient Hieroglyphics
Ancient Egyptian hieroglyphics originated as a sacred writing system used since around 3000 BC, combining alphabetic, logographic, and ideographic elements. It was used by priests, scribes, and educated citizens in different scripts until being replaced by Coptic in the 4th century AD. The Rosetta Stone, discovered in 1799, had the key to decrypting hieroglyphics by bearing a decree in three scripts: Greek, Demotic, and hieroglyphic. It was deciphered in 1822, unlocking understanding of ancient Egyptian writings.
DesignPeopleSystem
The document discusses various aspects of job design and work systems, including:
1. Job design involves specifying the content, tasks, responsibilities, and methods associated with a job.
2. Ergonomics and behavioral approaches aim to incorporate human factors and motivate workers through variety, autonomy, and feedback.
3. Traditional efficiency approaches focus on specialization and standardization of tasks from a productivity standpoint.
4. Effective job design balances both human and technical factors to optimize performance and satisfaction.
Organizational Behavior
Organizational behavior (OB) involves studying how individuals and groups function within organizations to accomplish work. Managers play an important role as they interact with others and direct activities to achieve organizational and personal goals. Manager's interpersonal skills are important because views of organizations are changing from seeing them as machines to seeing them as social systems where relationships among individuals are important. OB draws on various behavioral disciplines like psychology, sociology, and anthropology to study individual behavior, group behavior, organizational structure, and their impact on organizational effectiveness and efficiency.
Work Study Workshop
The document summarizes the key aspects of an on-campus work-study program, including eligibility requirements, the roles and responsibilities of various parties, and operational guidelines. It outlines the financial aid application process, how student earnings are subsidized, employment conditions, payroll procedures, and the auditing process to ensure compliance. Departments must submit agreements, post available positions, hire eligible students, and monitor hours worked, while the Work-Study Office reviews placements and provides guidance.
Workstudy
The document discusses various topics related to work study and productivity including:
- Defining work study, productivity, and their importance in analyzing work processes and identifying areas for improvement.
- Key aspects of work study such as method study, work measurement, incentive plans, and examining worker-machine relationships.
- Modeling work processes using tools like flow diagrams and developing standard times.
- The role of work study in broader areas like systems analysis, business process reengineering, and human-computer interface design.
Time And Motion Study
Time and motion studies are methods used to determine the optimal time it takes to complete tasks. They were developed by Frederick Taylor and the Gilbreths to establish fair work standards and eliminate unnecessary motions. While originally used in manufacturing, today time and motion studies can be applied to performance evaluations, planning, problem solving, and cost analysis in various organizations. The objective is to study jobs and determine standard times through observation, task breakdown, and time recording. Allowance factors are added to standard times to account for contingencies. However, studies may not always accurately capture real work conditions due to observer or worker issues.
More from ahmad bassiouny (20)
Recently uploaded
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
clinical examination of hip joint (1).pdf
described clinical examination all orthopeadic conditions .
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...Nguyen Thanh Tu Collection
https://app.box.com/s/qhtvq32h4ybf9t49ku85x0n3xl4jhr15The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
Leveraging Generative AI to Drive Nonprofit Innovation
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Recently uploaded (20)
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
Digital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental Design
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Scheduling And Htn
- 1. Practical Planning: Scheduling and Hierarchical Task Networks Chapter 12.1-12.2 CMSC 471 Adapted from slides by Tim Finin and Marie desJardins.
- 9. SIPE-2 Image from: http://www.ai.sri.com/~sipe/architecture.html
- 17. SATPlan
- 20. Now apply our favorite CSP solver!
- 23. Oil-Spill Response in SIPE-2 Image taken from http://www.ai.sri.com/~sipe/oil.html