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These tools are for learning and exploring concepts in artificial intelligence. They were developed at the?Laboratory for Computational Intelligence?at the?University of British Columbia?under direction of?Alan Mackworth?and?David Poole. They are part of the online resources for?Artificial Intelligence: Foundations of Computational Agents. If you are teaching or learning about AI, you may use these tools under the?terms of use. Feedback is welcome.

這些工具是用來學(xué)習(xí)和探索人工智能的概念。這套工具是由英國哥倫比亞大學(xué)計(jì)算智能實(shí)驗(yàn)室在Alan Mackworth和David Poole。它們是人工智能在線資源的一部分：計(jì)算智能體的基礎(chǔ)。如果你正在教授和學(xué)習(xí)有關(guān)AI的知識，則可以根據(jù)使用協(xié)議使用這些工具。歡迎提供反饋。

Search is an important part of AI; many problems can be cast as the problem of finding a path in a graph. This graph-searching tool is designed to help you learn about different search strategies.

搜索是人工智能的重要組成部分，許多問題都可以看作圖中的路徑搜索問題，圖搜索工具旨在幫助你學(xué)習(xí)不同的搜索策略。

Constraint satisfaction problems (CSPs) are pervasive in AI problems. A constraint satisfaction problem is the problem of assigning values to variables that satisfy some constraints. This tool lets you investigate arc consistency and domain splitting with backtracking as ways to solve these problems.

約束滿足問題（CDPs）在人工智能中普遍存在。約束滿足問題是滿足一些約束條件的變量賦值問題。這個工具可讓你調(diào)查弧一致性和使用回溯的域分割作為解決這些問題的方法。

This tool is designed to help you learn another strategy for solving CSPs. This tool demonstrates stochastic local search (various mixes of hill climbing and random moves) that walks through the space of total assignments trying to find an assignment with minimal error.

這個工具旨在幫助你學(xué)習(xí)求解約束滿足問題的另一個策略。這個工具演示隨機(jī)局部搜索（爬山和隨機(jī)移動的各種組合），它遍歷整個分配的空間，試圖找到一個誤差最小的分配。

Every representation and reasoning system needs a proof procedure in order to be complete. The purpose of this tool is to illustrate how the process of answer extraction within a knowledge base can be cast as a search problem. The deduction tool uses a language similar to Prolog and demonstrates its goal solving procedures.

每個表示和推理系統(tǒng)都需要一個證明程序才能完整。該工具的目的是闡述如何將知識庫中的答案提取過程轉(zhuǎn)換為搜索問題。演繹工具使用類似于 Prolog 的語言并演示其目標(biāo)求解過程。

Belief networks (also called Bayesian networks or causal networks) are a representation for independence amongst random variables for probabilistic reasoning under uncertainty. The purpose of this tool is to illustrate how probabilities are updated given new evidence in a belief network, and shows the details of how the variable elimination algorithm works.

信念網(wǎng)絡(luò)（也稱為貝葉斯網(wǎng)絡(luò)或因果網(wǎng)絡(luò)）是隨機(jī)變量之間獨(dú)立性的表示，用于在不確定性下進(jìn)行概率推理。該工具的目的是說明如何在給定信念網(wǎng)絡(luò)中的新證據(jù)的情況下更新概率，并顯示變量消除算法如何工作的詳細(xì)信息。

Learning is the ability to improve one's behaviour based on experience and represents an important element of computational intelligence. Decision trees are a simple yet successful technique for supervised classification learning. This tool demonstrates how to build a decision tree using a training data set and then use the tree to classify unseen examples in a test data set.

學(xué)習(xí)是根據(jù)經(jīng)驗(yàn)改善個人行為的能力，是計(jì)算智能的重要組成部分。決策樹是一種簡單而成功的監(jiān)督分類學(xué)習(xí)技術(shù)。該工具演示了如何使用訓(xùn)練數(shù)據(jù)集構(gòu)建決策樹，然后使用該樹對測試數(shù)據(jù)集中的未見示例進(jìn)行分類。

Inspired by neurons and their connections in the brain, neural networks are a representation used in machine learning. After running the back-propagation learning algorithm on a given set of examples, the neural network can be used to predict outcomes for any set of input values.

受神經(jīng)元及其在大腦中的連接的啟發(fā)，神經(jīng)網(wǎng)絡(luò)是機(jī)器學(xué)習(xí)中使用的一種表示。在給定的一組示例上運(yùn)行反向傳播學(xué)習(xí)算法后，神經(jīng)網(wǎng)絡(luò)可用于預(yù)測任何一組輸入值的結(jié)果。

A STRIPS problem is a planning problem that views the world as a set of variables and actions upon them. A problem is solved when a series of actions can be found that will lead to a Goal assignment of values to variables from a Start assignment. The STRIPS to CSP converter allows for the graphical creation of a STRIPS problem and then the conversion of said problem into a CSP for solving.

STRIPS 問題是一個規(guī)劃問題，它將世界視為一組變量和對其采取的行動。當(dāng)可以找到一系列動作，這些動作將導(dǎo)致從 Start 分配中將值分配給變量的 Goal 時(shí)，問題就解決了。STRIPS 到 CSP 轉(zhuǎn)換器允許以圖形方式創(chuàng)建 STRIPS 問題，然后將所述問題轉(zhuǎn)換為 CSP 以進(jìn)行求解。

轉(zhuǎn)自www.aispace.org