AI: Logic in AI 2
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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.
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Introduction
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The level of effort and\or cost required to create DRP can cause this project to have a low priority relative to other more immediate projects.
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DR coordinator.
Team leaders (there will be several groups and possibly subgroups).
Team members.
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What length of time is acceptable for recovery?
What amount of data loss is acceptable?
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What are the dependencies on other systems?
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Data transfers.
Manual processes
Remote processing
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Because there is interaction with dependencies on other systems and user interface, and the sensitivity of the data.
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Everyone involve with this effort (including upper management within a company) needs to have a single vision of what success look like, without this you risk wasting time and money on a plan that may be viewed as a failure.
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These people may not be part of the DR team, but they are important. (For example who has the authority to declare a disaster?)
This list should be maintained both by name and by role; it should be validated and updated frequently.
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1. Management is defined as the process of organizing work through people to efficiently achieve goals.
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3. Planning is the systematic development of action programs to reach objectives by analyzing opportunities.
4. Organizing involves arranging resources like people, materials, and technology in an orderly way to accomplish plans and objectives.
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3. Planning is the systematic development of action programs to reach objectives by analyzing opportunities.
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2. Representing planning problems using STRIPS and situation calculus.
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The document introduces Logical Framework Analysis (LFA), a methodology used for participatory project planning, implementation, and evaluation. It describes the key steps in LFA, including situation analysis, stakeholder analysis, problem analysis, objective analysis, strategy analysis, and developing a project planning matrix. The planning matrix outlines objectives, indicators, means of verification, and external assumptions. The document provides definitions for the different components of the LFA process and planning matrix.
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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.
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Sumanth Lagadapati
[email protected]
Introduction
Many companies don’t have a disaster recovery plan often there is a desire for a DRP.
The level of effort and\or cost required to create DRP can cause this project to have a low priority relative to other more immediate projects.
A DRP is viewed as "nice to have" or "just insurance that will not be used ", and not as a critical business component.
That is, until there is a failure that causes a significant outage or loss of data (often at a significant cost to the business).
It is my opinion that every company could benefit from both a disaster recovery plan and a business continuity plan (BCP)
Investing in a DRP and BCP is just as an important for most business in my opinion.
Where do you start?
The first step is to create a DR team and this includes an:
Executive sponsor.
DR coordinator.
Team leaders (there will be several groups and possibly subgroups).
Team members.
This people should be designated as either primary or backup for position, with every position having more than one person assigned this to minimize people as a single point of failure.
The goal is to have the expertise to help develop the various recovery procedures, and is committed to success of the overall effort.
Where do you start? cont…
The next step is to define business goals.
The goal should address items such as:
What functional areas need to be recovered?
What length of time is acceptable for recovery?
What amount of data loss is acceptable?
This often involves prioritization and a cost-benefit analysis to determine the worth of recovery (i.e. something that may be premature at this phase of the project).
Understand the business goals and objectives
To find out what that really entails you must know:
What are the critical systems?
What are the key processes and applications?
What are the dependencies on other systems?
This includes:
Data transfers.
Manual processes
Remote processing
Then documents these processes.
Because there is interaction with dependencies on other systems and user interface, and the sensitivity of the data.
Once the systems have been identified, attempt to quantify their impact relative to the overall business goals.
Identify specific requirements
Everyone involve with this effort (including upper management within a company) needs to have a single vision of what success look like, without this you risk wasting time and money on a plan that may be viewed as a failure.
Identify key personnel
These people may not be part of the DR team, but they are important. (For example who has the authority to declare a disaster?)
This list should be maintained both by name and by role; it should be validated and updated frequently.
Identify single point of failure
The overall goal of this step is to mitigate unnecessary risk.
The scope of this effort includes people, software, equipment, and infrastructure.
It i.
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A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
There is a handout to go with this module, a Logframe with blanks. http://www.slideshare.net/Makewa/exercise-watsan-logframe-with-blanks
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Linear programming is a technique for optimizing a linear objective function subject to linear equality and inequality constraints. It was developed by George Dantzig in 1947 and has wide applications in fields like production, marketing, and finance. A linear programming problem involves decision variables that have a linear relationship to the objective and constraints. It aims to find the optimal values for the variables that maximize or minimize the objective subject to the constraints. While linear programming is useful for optimization problems, it has limitations such as requiring relationships and parameters to be linear and constant.
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The document provides an introduction to operation research techniques, specifically linear programming. It discusses the key components of a linear programming model, including decision variables, the objective function, and constraints. It also outlines the basic assumptions required for linear programming, such as certainty of parameters, proportionality of relationships, divisibility of resources, and optimization. The document serves to introduce readers to the structure and assumptions of linear programming as an operations research technique.
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Abstract: Lexicographic Linear Goal programming within a pre-emptive priority structure including Column-dropping Rule has been one of the useful techniques considered in solving multiple objective problems. The basic ideas to solve goal programming are transforming goal programming into single-objective linear programming. An optimal solution is attained when all the goals are reached as close as possible to their aspiration level, while satisfying a set of constraints. One of the Goal Programming algorithm – the Lexicographic method including Column-dropping Rule and the method of LINGO software are discussed in this paper. Finally goal programming model are applied to the actual management decisions, multi-objective programming model are established and used LINGO software and Column-dropping Rule to achieve satisfied solution.Keywords: Goal programming, Lexicographic Goal programming, multi-objective, LINGO software, Column-dropping Rule.
Title: Research on Lexicographic Linear Goal Programming Problem Based on LINGO and Column-Dropping Rule
Author: N. R. Neelavathi
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
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Data Mining: Text and web mining
This document discusses text and web mining. It defines text mining as analyzing huge amounts of text data to extract information. It discusses measures for text retrieval like precision and recall. It also covers text retrieval and indexing methods like inverted indices and signature files. Finally, it discusses challenges in web mining like the huge size and dynamic nature of the web and how web usage mining allows collection of web access information from server logs.
Data Mining: Outlier analysis
Outlier analysis is used to identify outliers, which are data objects that are inconsistent with the general behavior or model of the data. There are two main types of outlier detection - statistical distribution-based detection, which identifies outliers based on how far they are from the average statistical distribution, and distance-based detection, which finds outliers based on how far they are from other data objects. Outlier analysis is useful for tasks like fraud detection, where outliers may indicate fraudulent activity that is different from normal patterns in the data.
Data Mining: Mining stream time series and sequence data
This document discusses various methodologies for processing and analyzing stream data, time series data, and sequence data. It covers topics such as random sampling and sketches/synopses for stream data, data stream management systems and queries, the Hoeffding tree and Very Fast Decision Tree (VFDT) algorithms for classification, ensemble methods and concept drift, clustering of evolving data streams, trend analysis and similarity search for time series data, Markov chains for sequence analysis, and algorithms like the forward algorithm, Viterbi algorithm, and Baum-Welch algorithm for hidden Markov models.
Data Mining: Mining ,associations, and correlations
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets based on minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Data Mining: Graph mining and social network analysis
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
Data Mining: Data warehouse and olap technology
This document discusses data warehousing and online analytical processing (OLAP) technology. It defines a data warehouse, compares it to operational databases, and explains how OLAP systems organize and present data for analysis. The document also describes multidimensional data models, common OLAP operations, and the steps to design and construct a data warehouse. Finally, it discusses applications of data warehouses and efficient processing of OLAP queries.
Data MIning: Data processing
Data processing involves cleaning, integrating, transforming, reducing, and summarizing data from various sources into a coherent and useful format. It aims to handle issues like missing values, noise, inconsistencies, and volume to produce an accurate and compact representation of the original data without losing information. Some key techniques involved are data cleaning through binning, regression, and clustering to smooth or detect outliers; data integration to combine multiple sources; data transformation through smoothing, aggregation, generalization and normalization; and data reduction using cube aggregation, attribute selection, dimensionality reduction, and discretization.
Data Mining: clustering and analysis
Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods like CLIQUE and PROCLUS. Constraint-based clustering allows users to specify preferences or constraints.
Data mining: Classification and Prediction
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule analysis, and ensemble methods like bagging and boosting. It also discusses key considerations for classification and prediction like accuracy, speed, robustness, and scalability. The goal is to construct models or classifiers that can predict categorical labels for classification tasks or continuous values for prediction tasks.
Data Mining: Data mining classification and analysis
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance depends on algorithm efficiency and scalability for large datasets across diverse database types.
Data Mining: Data mining and key definitions
Data mining is the process of analyzing large amounts of data to discover hidden patterns and relationships. It involves collecting data, removing noise, focusing on relevant portions, analyzing the data, and extracting useful information. Key components of a data mining system include a data warehouse server, information repository, user interface, data mining engine, and pattern evaluation module. A data warehouse is a centralized repository where data is stored and organized for analysis from multiple sources.
Data Mining: Data cube computation and data generalization
Data generalization abstracts data from a low conceptual level to higher levels. Different cube materialization methods include full, iceberg, closed, and shell cubes. The Apriori property states that if a cell does not meet minimum support, neither will its descendants, and can reduce iceberg cube computation. BUC constructs cubes from the apex downward, allowing pruning using Apriori and sharing partitioning costs. Discovery-driven exploration assists users in intelligently exploring aggregated data cubes. Constrained gradient analysis incorporates significance, probe, and gradient constraints to reduce the search space. Attribute-oriented induction generalizes based on attribute values to characterize data. Attribute generalization is controlled through thresholds and relations.
Data Mining: Applying data mining
This document discusses different types of data mining including object mining, spatial mining, text mining, web mining, and multimedia mining. It describes how data mining can be used to analyze object-relational and object-oriented databases by generalizing set-valued attributes, aggregating and approximating spatial and multimedia data, generalizing object identifiers and class hierarchies. The document also discusses spatial databases, spatial data mining, spatial data warehouses, mining spatial associations and co-locations, mining raster databases, multimedia databases, and approaches for multimedia data mining and analysis.
Data Mining: Application and trends in data mining
This document provides an overview of application trends in data mining. It discusses how data mining is used for financial data analysis, customer analysis in retail and telecommunications, biological data analysis, scientific research, intrusion detection, and more. It also outlines statistical and visual data mining techniques, security considerations, and how to assess a data mining system. The document explores both current and emerging trends in data mining across various domains.
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 the appropriate actions based on the conditions that are sensed to be true. 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.
AI: Logic in AI
The document discusses different types of logical reasoning systems used in artificial intelligence, including knowledge-based agents, first-order logic, higher-order logic, goal-based agents, knowledge engineering, and description logics. It provides examples of objects, properties, relations, and functions that can be represented and reasoned about logically. It also compares different approaches to logical indexing and storage of knowledge bases.
AI: Learning in AI 2
Bayesian learning views hypotheses as intermediaries between data and predictions. Belief networks can have known or unknown structures and hidden variables. Belief networks represent knowledge locally while neural networks use distributed representations. Reinforcement learning uses rewards to learn successful agent functions, like Q-learning which learns action-value functions. Active learning agents consider actions, outcomes, and how actions affect rewards received to build an environment model and utility function. Explanation based learning speeds up programs by reusing results of prior computations to make knowledge bases more efficient.
AI: Learning in AI
Neural networks can be used for machine learning tasks like classification. They consist of interconnected nodes that update their weight values during a training process using examples. Neural networks have been applied successfully to tasks like handwritten character recognition, autonomous vehicle control by observing human drivers, and pronunciation of written text. Their design is inspired by biological neural networks in the brain.
AI: Introduction to artificial intelligence
This document provides an introduction to artificial intelligence including definitions of AI, categories of AI systems, requirements for an artificially intelligent system, a brief history of AI, examples of AI in the real world, definitions of intelligent agents and different types of agent programs. It defines AI as the study of intelligent behavior in computational processes and discusses systems that think like humans, think rationally, act like humans, and act rationally. It also outlines the requirements for an AI system to have natural language processing, knowledge representation, automated reasoning, and machine learning.
AI: AI & problem solving
The document discusses various problem solving techniques in artificial intelligence, including different types of problems, components of well-defined problems, measuring problem solving performance, and different search strategies. It describes single-state and multiple-state problems, and defines the key components of a problem including the data type, operators, goal test, and path cost. It also explains different search strategies such as breadth-first search, uniform cost search, depth-first search, depth-limited search, iterative deepening search, and bidirectional search.
More from Datamining Tools (20)
Data Mining: Mining stream time series and sequence data
Data Mining: Mining stream time series and sequence data
Data Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlations
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Data mining classification and analysis
Data Mining: Data mining classification and analysis
Data Mining: Data cube computation and data generalization
Data Mining: Data cube computation and data generalization
Data Mining: Application and trends in data mining
Data Mining: Application and trends in data mining
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AI: Logic in AI 2
- 1. Logic in AI 2
- 2. A simple Planning Agent A simple planning agent uses the percepts provided by the environment to build a complete and correct model of the current world state, after which , to achieve its goal, it calls a suitable planning algorithm (which we will call IDEAL-PLANNER) to generate a plan of action.
- 3. Problem solving and Planningby a simple planning agent Basic elements of a search-based problem-solver are Representation of actions, Representation of states, Represents of goals and Representation of plans.
- 4. Components of practical planning (1) Restrict the language with which PLANNER we define problems. With a restrictivelanguage, there are fewer possible solutions to search through. (2) Use a special-purpose algorithm called a planner rather than a general-purposetheorem prover to search for a solution.
- 5. Basic representation for planning LEAST COMMITMENT this principle says that one should only make choices about things that you currently working. PARTIAL ORDER A planner that can represent plans in some steps are ordered with respect to each other and other steps are unordered is called a partial order planner. LINEARIZATION A totally ordered plan that is derived from a plan P by adding ordering constraints is called a linearization of P.
- 6. What is a PLAN? A plan is formally defined as a data structure consisting of the following four components: Set of plan steps Set of step ordering constraints Set of variable binding constraints Set of casual links
- 7. What is a Solution? A solution is a plan that an agent can execute, and that guarantees achievement of the goal. If we wanted to make it really easy to check that a plan is a solution, we could insist that only fully instantiated, totally ordered plans can be solutions.
- 8. How to Resolve threats in planning? Resolve now with an equality constraint Resolve now with an inequality constraint Resolve Later
- 9. Knowledge Engineering for planning Decide what to talk about. Decide on a vocabulary of conditions (literals), operators, and objects. Encode operators for the domain. Encode a description of the specific problem instance. Pose problems to the planner and get back plans.
- 10. Practical Planning Hierarchical decomposition The practical planners have adopted the idea of hierarchical decomposition: that an abstract operator can be decomposed into a group of steps that forms a plan that implements the operator. These decompositions can be stored in a library of plans and retrieved as needed
- 11. Analysis of Hierarchical Decomposition Abstract solution A plan that contains abstract operators, but is consistent and complete once an abstract solution is found we can prune away all other abstract plans from the search tree. This property is the downward solution property. We can prune away all the descendants of any inconsistent abstract plan. This is called theupward solution property
- 12. Resource constraints in planning Using measures in planningThe solution is to introduce numeric-valued measures. Measures such as the price of gas are realities with which the planner must deal, but over which it j has little control. Other measures, such as Cash and Gas Level, are treated as resources that can be produced and consumed. Temporal constraintsIn most ways, time can be treated like any other resource. The initial state specifies a start time for the plan.
- 13. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net