Ⅹ. Harbor

Relation of Terms Term includes Properties Term can be Algorithm Use task constraints to make search efficient. Perform Inference with First Order Logic and appreciate the strengths and weaknesses of this and other logic representations (eg Propositional). Use PDDL to plan and execute actions using either Propositional or First Order Logic representations.https://discover304.top/2021/03/01/30-0-ra-note/#template Create and reason with a representation of a Bayesian agent for handling a non-deter ...

什么是Gson? Gson（又称Google Gson）是Google公司发布的一个开放源代码的Java库，主要用途为序列化Java对象为JSON字符串，或反序列化JSON字符串成Java对象。而JSON(JavaScript Object Notation) 是一种轻量级的数据交换格式，易于人阅读和编写，同时也易于机器解析和生成，广泛应用于各种数据的交互中，尤其是服务器与客户端的交互。 注：序列化：Serialization，反序列化：Deserialization 参考资料导航 Gson全解析：https://www.jianshu.com/p/fc5c9cdf3aab

迷失在了历史的长河中。Lost in time.

What is flask? Python web application framework. Why flask? lightweight ability to scale up. How to use basic functionality of flask?Here we list the basic functionality of flasj. Install and activation1234$ mkdir myproject$ cd myproject$ python3 -m venv venv$ . venv/bin/activate 12PS C:\path\to\app> py -m venv venvPS C:\path\to\app> venv\Scripts\activate Real code example123456from flask import Flaskapp = Flask(__name__)@app.route('/')def hello_world(): return 'Hello ...

Lecture 13/14/15 VV&C Line coverage/ branch coverage Bug tracking review, static analysis deploy maintenance DevOps, communication delay Waterfall model, Spiral model, The Unified Process

concepts as mental representations of classes objects or events. They determine how things are related or categorized. concepts: improve cognitive economy, help us make useful predictions and generalizations, communicate. cognitive economy: prototype theory, classical theory, pros: intuitive, economical, definitions are easy to communicate, easy to check category membership against definition. cons: hard to find satisfactory definitions, borderline/debatable cases, typicality effects. cate ...

Tools Collection Monitoring Hierarchical planning Hierarchical task network (HTN) High level action (HLA) refinement Template12345<!-- refinements -->Refinment({ActionName}(variable1, variable2), Precond: {constraints} Steps: {action list}) Monitoring and replanning in unbounded num-determinismExecution monitoring includes action monitoring, plan monitoring. Hierarchical Planninghierarchical decomposition, Hierarchical task network (HTN) planning: ini ...

Tools Collection search-based problem-solving agent logic-based planning agent Planning definition domain language (PDDL) state-space search forward state-space search backward state-space search plan-space search partial-order planning Template123456<!-- PDDL -->Initial State: {state}Goal State: {state}Action({ActionName}(variable1,variable2,...) Precond:{a conjunction, all positive}, Effect:{a conjunction, can extend to disjunction ...

CFG and syntax treecontext-free languages are common to both artificial languages and natural languages, can be resolved by syntax tree, which displays the grammatical consistent structure of a language text and constructing the tree is a step in many language processing (LP) tasks. grammar specify what syntax tree is possible, consist of terminals of grammar or the leaf of syntax and non-terminals that not appear in sentences of language. generating syntax tree from grammar, beginning with star ...

Matplotlib几个基本的颜色代码blue( b ), cyan( c ), green( g ), black( k ), magenta( m ), red( r ), white( w ), yellow( y ) Matplotlib颜色对照表 颜色及十六进制对应123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141cnames = {'aliceblue': ...

Lecture 10.1 CommunicationKey linkage Communication is a goal: to transfer information from sourceto destination. If M and F are independent, $H(M|F) = H(M)$. If F deterministically map to M, $H(M|F) = 0$. Key termscommunication, information, source, destination, computational level goal, necessary subgoal source, probability distribution over meaning, transmitter, encoder, function, meaning, form, receiver, decoder, noise source, reference resolution information theory, information c ...

Key termsthe mapping problem, mutual exclusivity, inductive bias, word single object, fast mapping, word, object, observation, cross-situational word learning, sorting, reasoning, word object, co-occurrence statistics, word referent mapping, intension model, cross-situational information, human simulation paradigm, propose-but-verify, hypothetical mapping, intentional reasoning inductive bias, mutual exclusive bias, whole object bias, taxonomic bias, exposure, testing, meaning error, overextensi ...

Key termsthe mapping problem, mutual exclusivity, inductive bias, word single object, fast mapping, word, object, observation, cross-situational word learning, sorting, reasoning, word object, co-occurrence statistics, word referent mapping, intension model, cross-situational information, human simulation paradigm, propose-but-verify, hypothetical mapping, intentional reasoning inductive bias, mutual exclusive bias, whole object bias, taxonomic bias, exposure, testing, meaning error, overextensi ...

绝赞偷懒

AbstractThis note collect all terms occur in SEPP Lecture 6/7/9. It would be helpful to recap what has learnt and what has already been abstracted away. Pool of atomic termsplan-driven development process, heavy documentation, formal documentation, iteration, agile development process, individual, interaction, working software, customer collaboration, responding to change, design, implementation, unfinished feature, requirement engineering, requirement specification document, omniprese ...