Look thru myself..

I sometimes feel like dunno myself, my heart, under certain circumstances, i do able to act totally different from my normal personality. I guess this is what He means when David said: Test my heart, make my heart pure... We need to explore ourself and purify it, try to recognize the creature inside.. Bible said human heart is basicly evil, it is wicked, only thru Jesus and His salvation, then this heart is renewed, converted into a pure heart.

A recent conversation between me and my old fren has revealed this. She (my fren) is facing a difficulties in her workplace, office politic and extremely long hours overtime. Cause i knew this gal since i was in primary, i know her character and personality (which is quite similar to mine, phlghm, melancholic, sangguin a bit), it is quite amazed me that she said she has done something bad w/o she mean to. Looking at that, and reflecting my experience also, i can understand it, how we human being sometimes are able to let go some of our value, principal in certain circumstances and do something opposite. Pressure from others and pain (in heart) is something that should be overcome. It sharpen you. a kind of make you mature and have a broad knowledge. This life is so colourful is it.

Lets move to other topic, since last week i ve been studying 3 subjects for exam, i found them interesting. This is the topics which are related to Artificial Intelegence.
Artificial intelligence
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"AI" redirects here. For other uses, see AI (disambiguation).

Garry Kasparov playing against Deep Blue, the first machine to win a chess match against a reigning world champion.
Artificial intelligence (AI) is both the intelligence of machines and the branch of computer science which aims to create it.
Major AI textbooks define artificial intelligence as "the study and design of intelligent agents,"[1] where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.[2] John McCarthy, who coined the term in 1956,[3] defines it as "the science and engineering of making intelligent machines."[4]
Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.[5] General intelligence (or "strong AI") has not yet been achieved and is a long-term goal of AI research.[6]
AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic.[7] AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.[8]


The most interesting is CBR (Case-based Reasoning), a method of problem solving technique based on a very simple human's learning prosess, as to find solution from similar past problems.

Case-based reasoning
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Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent kind of analogy making.
It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving. Or, more radically, that all reasoning is based on past cases experienced or accepted by the being actively exercising choice – prototype theory – most deeply explored in human cognitive science.

Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process[1]:
Retrieve: Given a target problem, retrieve cases from memory that are relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case.

Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries.

Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan.

Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his newfound procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands.

Problem which are suitable for as a CBR appplication is Help Desk Information as to assist customer of their queries, more to similar problem that the officer handle everyday. This is very efficient and functional for this typical of problem.


The other problem solving technique that i learn is also interesting, Genetic Algorithm, which you can find it in the below information:

From wikipedia

A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

Application of GA is for a problem like job shop scheduling, as for other optimization problem in real-life application. The most interesting feature in Genetic Algorithm is Adaptation, as we are able to adapt and change our system manually in order to get a further improvement performance of it.

For more info of those two techniques:
http://www.aiai.ed.ac.uk/links/cbr.html
http://www.ai-cbr.org/

http://www.obitko.com/tutorials/genetic-algorithms/
http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/GeneticAlgorithms