Network Genius Android Download
V 5.0
The Chinese version of Internet Genius is a very free level-breaking game. There will be a large number of modes that are constantly opened here. Players will have different experiences here. All operations are very free and will not limit players. Any choice will give you a different feeling.
1. Become the number one player
Challenge other players on the leaderboard to see who is better. You can write your name in the latest super award or hall of honor.
2. Find the Aki Award
Akinator invites you to imagine your way to black gold, platinum, gold... awards and a place in the Hall of Fame. As you know, Akinator loves guessing characters and taking on harder challenges. To do this, you need to ask him to guess forgotten characters, those that no one has played for a long time. Check the rankings and strive for the top!
3. Unleash your creativity
You can use Geniz Points to unlock and customize the Akinator image you want. Internet geniuses can become kings, cowboys, or musicians. Use 12 hats and 13 pieces of clothing to create your own unique combination.
4. Continue to participate in daily challenges
You can find five mystery characters every day to win extra or special Aki prizes. Complete all daily challenges to earn the gold Daily Aki Award, the most prestigious Aki Award!
5. Play freely without restrictions
The ultimate potion unlocks all characters and removes all ads so you can enjoy the best gaming experience.
1. By solving more puzzles in these missions, you can unlock more levels in this game.
2. During the mobile game, as the user solves different tasks, the background sound will change, and the content of the entire mobile game is very interesting.
3. There are many challenging tasks in the Internet Genius mobile game. In the game, players need to flexibly choose which parts to remove.
1. The most basic implementation method: keyword matching
Build a keyword thesaurus, perform keyword matching on the sentences entered by the player, and then call the corresponding knowledge base.
The entry barrier for this method is very low, and basically any programmer can implement it. For example, the current smart reply on WeChat public platform and the filtering of sensitive words on many websites are like this.
However, there are many problems with this approach, such as
a. Since it is keyword matching, if there are multiple keywords in the sentence entered by the player, due to issues such as keyword weight (compared with the keywords in the knowledge base) and other issues, the keyword matching method will not work at this time. Good at it
b. There is no understanding of the semantics of the player's input sentences, resulting in incorrect answers. Of course, when it comes to products, we use cute ways to avoid answering questions that we can’t answer.
c. There is basically no self-learning ability. The rules can only be maintained entirely manually, and the rules are basically fixed.
d. Poor performance and scalability. Again, the above example contains multiple keywords in one sentence. Using ordinary programming language to do keyword matching has extremely poor performance. Even if some text processing algorithms are used (such as Double-array trie tree), it is difficult to meet the needs of large-scale scenarios.
2. A slightly more advanced implementation method: based on search engines, text mining, natural language processing (NLP) and other technologies
Compared with 1 keyword matching, the core problem to be solved by this implementation method can be roughly understood as: based on the semantics of a short text (such as a sentence asked by the player), infer the most likely intention of the player, and then from Find the most similar results among the massive knowledge base content.
The specific technical implementation will not be detailed. Let me give a very rough example to briefly explain the idea of this implementation method (not rigorous, just to illustrate the idea).
If the player asks: What is the temperature in Beijing the day after tomorrow?
If you adopt the idea of a pure search engine (the ideas based on text mining and NLP are different, but you can refer to this idea), the actual process is divided into several steps.
1. Segment the input sentence and obtain three keywords: Beijing, the day after tomorrow, and temperature. When segmenting words, pre-built industry lexicon is used. Beijing matches the pre-built city database, the day after tomorrow matches the date database, and the temperature matches the weather database.
2. Match the above word segmentation results with the rule base according to a certain algorithm to obtain the rule with the highest matching degree. Assume that there is a weather rule in the rule library: city library + date library + weather library, so it can be roughly inferred that the player may want to ask about the weather in a certain place on a certain day.
3. Make a detailed analysis of the semantics. We know that the city is Beijing, the date is the day after tomorrow, and the knowledge to be acquired is weather forecast.
4. Call third-party weather interfaces, such as China Weather Network - professional weather forecast, meteorological service portal data
The magic lamp master can guess what you are thinking, which is very interesting to play.
