analogical reasoning

Devi Parikh, chief scientist of the Facebook AI Research Institute (FAIR), is the 2017 IJCAI computer and thought Award winner (Ijcai, one of the two most important awards, known as the "Fields Award" in the International AI Field) and ranked Forbes 2017 "20 Women in the AI research list. She is mainly engaged in computer vision and pattern recognition research, including computer vision, language and vision, general reasoning, artificial intelligence

system Characteristics of 6.1.1 Expert system 1. Definition Expert system is a large number of experts in a field of expertise in the knowledge and experience of intelligent system, can use the knowledge of human experts and problem-solving methods to deal with this area of problems. In short, expert system is a computer program system that simulates human experts to solve domain problems. 2. Expert System Features Heuristic: Expert system can use expert knowledge and experience to make inferen

and framework system, woods and brachman proposed a KL-ONE knowledge expression system. A large number of KL-ONE-based knowledge expression languages and practical systems emerged, such as: nikl, loom, classic, back, Kandor, kryton and so on; from the beginning of the 1990s s to the present: the expression of description logic as the basic knowledge has become the mainstream, the KL-ONE family gradually evolved into the current description logic (Germany dfki ). From a historical perspective, t

activities. Some problems can only be solved by recursive methods. For some problems, recursive methods are more effective than other methods. In computer programs, recursion can constitute an automatic reasoning mechanism. One of the main problems of artificial intelligence is to enable computers to have the function of automatic reasoning. After a "recursive mechanism" is established, the computer can pe

understanding.In the inference of Proposition p to proposition Q, we often rely on relativity rather than causality. If causality, then its reasoning process should be a rigorous proof process, but we rarely do such proof. By contrast, we are more inclined to "find the law" in such a way as to get the inference that inductive reasoning. It's like relying on neural network algorithms to train a formula that

the product of time and efficiency. You can "put" a lot of time on one thing, but find that there is no progress, because you do not spend all day doing what you want to do, what you want to learn is resident in your brain, always giving it the highest priority. When you are walking, when you are eating, when you dream, it is this thing that hoping at, your CPU is always assigned to it, this time your thinking time is used to the extreme, the time you put into really equals the actual elapsed t

project, shouted "AI = RL + DL", thinking that the DRL of the combination of DL's representation and RL's reasoning ability would be the ultimate answer to AI.The number of RL papers grew rapidly [8]the reproducible crisis of 1.1 DRLHowever, researchers have begun to rethink DRL in the last six months. Many algorithms are difficult to reproduce due to the fact that important parameter settings and engineering solution details are often not available

example, 3 indicates that the latest execution date is 3 days later) follow the rules from the highest priority to the bottom: If M. Todo (T1), D = 3 If M. Todo (T2), D = 7 If M. Todo (T3), D = 10 If M. Todo (T4), D = 12 If M. Todo (T5), D = 14 The function is to obtain the test item and test time for each device based on the given rules and a group of devices. Even if you do not consider the changes in rules, it is very tedious and tedious to implement such bus

comparison of the values in the class, do not directly compare two values with equals, for example:Double A = 1;Double b = 1;System.out.prinln (A.equals (b)); It is clear that this statement is wrong.The Java language has the following requirements for Equals ():A: Symmetry: if X.equals (y) returns "true", then Y.equals (x) should also return "true".B: reflectivity: X.equals (x) must return is "true".C: analogical: If X.equals (y) returns "true" and

good, enter the corresponding name and value, and then click OK. Next. Explanations from the Internet:Archetypecatalog represents the plug-in uses the archetype metadata, does not add this parameter when the default is Remote,local, that is, the central warehouse archetype metadata, because the central warehouse archetype too much, so the result is very slow, Specify internal to indicate that only internal metadata is used.5. For example, fill out project name and module name, module name is t

people, make your thinking more abstract (9) Metaphor (analogical technology) links abstract concepts with concrete, everyday visible things, Easier to understand, clear metaphors make the code easier to understand and expand (Ten) PO technology: Two words are randomly combined, through metaphor (heterogeneous Association) (11) system metaphor: Any software system should be able to be described by appropriate metaphors. (12) A metaphor can guide the

present, do we have it? In fact, I personally have a bold speculation, I think our ancestors of the "Art of War" is a good design model, because it conforms to the design pattern needs of the basic characteristics of the specific conditions, in a certain way reasonable and efficient solution to the problem. Only one is used in the military, the second is the completeness of the aspects we have not studied. But I think we are at least not very good at expanding and using

Reverse Inference The enabling of reverse inference rules occurs when your program asks about Pyke. For example, require Pyke to prove a specific target. Pyke certification activities only use the activated rule repository.Overview of "reverse inference" For reverse reasoning, Pyke needs to find out the rule that the then clause part matches the target (that is, the question the program asks Pyke. After finding a matched rule, Pyke tries to prove tha

engines for implementation, you may need to change the rule definition. Rule-based Expert System (RBEs)An expert system is a branch of artificial intelligence. It imitates human reasoning methods, uses exploratory methods for reasoning, and uses terminologies that humans can understand to explain and prove their reasoning conclusions. Expert Systems are classif

PrefaceRecently, it is very interesting to some reasoning problems, at first it originates from learning Mackay "Information Theory, Inference and learning algorithm", it is a core methodology of this subject to feel data mining and inference analysis. Later I felt that it was important for me to survive and develop in the information age. For the information age, the so-called "asymmetric information" has gradually evolved from the asymmetry of acces

rules, draw a conclusion. This conclusion may be a static result or a set of actions that need to be performed. The process of applying this rule is called reasoning. If the process of reasoning is handled by the program, then the program is called the Inference engine/inference machine. The inference engine adopts different control strategies depending on the knowledge representation, and common types inc

[Classic paradox roaming (bottom)] This is the third part: the paradox caused by the premise not self-consistency and the paradox encountered by the right change. (5) paradox caused by non-self-consistency of premises Here we will see that if the premise is not self-consistent, the conclusions will not be self-explanatory, or even absurd or no conclusion. 5-1 "Russell is the pope" Logically speaking, absurd assumptions can deduce any absurd conclusion, even if the

network, which consists of a decoder hierarchy that corresponds to each encoder. Where the decoder uses the max-pooling that is accepted from the corresponding encoder Indices the nonlinear upsampling of the input feature graph. This idea comes from the architecture designed for unsupervised functional learning. Reusing max-pooling in a decoding network Indics has several practical benefits: (1) It improves the boundary Division (2) reduces the number of parameters for end-to-end training (3) T

1) (in the debate on the basic problems of mathematics and the relationship between mathematics and logic, there are three schools) logical schools: logic prevails over mathematics. Mathematics can be obtained completely from pure logic without the introduction of the original word term and the assumption of the plus. The idea of this school is embodied in Russell's masterpiece mathematical principles. 2) the formalism school holds that mathematics itself is a pile of formal systems, each of

compares the user's query keywords with each word in the full text, the semantic match between the query request and the document is not considered. Based on the knowledge of ontology, this paper proposes a semantic search engine Model Based on Ontology. This model can perform Knowledge-Based Reasoning Based on the user's query keyword or question, so as to improve the relevance of the search results and achieve a certain level of semantic search. 2