Tsarin Blockchain don Lissafin AI: Haɗa Hujjar-Aiki tare da Koyo Mai Ƙarfafawa

Teburin Abubuwan Ciki

1. Gabatarwa

Fasahar Blockchain ta kawo sauyi ga masana'antu daban-daban tun lokacin da aka gabatar da Bitcoin, tana ba da hanyoyin amincewa masu rarrabawa ta hanyar algorithms na yarjejeniya kamar hujjar-aiki. Duk da haka, tsarin hujjar-aiki na al'ada yana cinye albarkatun lissafi masu yawa don warware wasu takalmamin lissafi marasa ma'ana, wanda ke haifar da ɓarnatar makamashi mai yawa da kuma damuwa game da muhalli.

Wannan takarda tana ba da shawara game da wani sabon tsari wanda ke canza hujjar-aiki zuwa matsalar koyo mai ƙarfafawa, inda nodes na blockchain ke haɗin gwiwa don horar da cibiyoyin jijiyoyi masu zurfi yayin kiyaye tsaron cibiyar sadarwa. Wannan hanyar tana magance babban iyaka na tsarin blockchain na al'ada ta hanyar sanya aikin lissafi ya zama mai ma'ana kuma mai amfani ga ƙalubalen AI na ainihi.

2. Hanyar Aiki

2.1 Blockchain a matsayin Tsarin Yarjejeniya na Markov

Ana ƙirƙira tsarin girma blockchain a matsayin Tsarin Yarjejeniya na Markov (MDP) inda:

• Yanayi (S): Yanayin blockchain na yanzu wanda ya haɗa da ma'amaloli, tubalan baya, da yanayin cibiyar sadarwa
• Aiki (A): Zaɓin sigogin toshe na gaba da ɗimbin bayanan horo
• Lada (R): Haɗin nasarar tabbatar da toshe da ci gaban horon samfuri
• Canji (P): Canjin yanayi wanda aka ƙaddara ta hanyar yarjejeniya da yaduwar cibiyar sadarwa

2.2 Haɗin Koyo Mai Zurfi Mai Ƙarfafawa

Muna haɗa cibiyoyin sadarwa masu zurfi (DQN) tare da tsarin yarjejeniya na blockchain, inda nodes ke fafatawa don warware matsalolin koyo mai ƙarfafawa maimakon takalmamin sirri. Wakilin koyo yana yanke shawara mafi kyau akan yanayin muhalli, ana ƙara sabbin tubalan kuma ana tabbatar da su ta wannan tsari.

3. Aiwar Fasaha

3.1 Tsarin Lissafi

An ayyana aikin manufa na koyo mai ƙarfafawa kamar haka:

$J(\theta) = \mathbb{E}_{(s,a) \sim \rho(\cdot)}[\sum_{t=0}^{\infty} \gamma^t r_t | s_0 = s, a_0 = a]$

Inda $\theta$ ke wakiltar sigogin cibiyar jijiyoyi, $\gamma$ shine ma'aunin rangwame, kuma $\rho$ shine rarraba aiki-yanayi.

Dokar sabuntawa ta Q-learning ta haɗa da lada na musamman na blockchain:

$Q(s,a) \leftarrow Q(s,a) + \alpha[r + \gamma \max_{a'} Q(s',a') - Q(s,a)]$

3.2 Ƙirar Hanyar Yarjejeniya

Hanyar yarjejeniya ta haɗa:

• Canje-canjen yanayi na ƙaddara daga girma blockchain
• Bazuwar a cikin zaɓin aiki daga dabarun bincike
• Rikitarwar lissafi na horon cibiyar jijiyoyi mai zurfi

4. Sakamakon Gwaji

Ma'aunin Aiki

Gwaje-gwajenmu sun nuna gagarumin ci gaba akan tsarin hujjar-aiki na al'ada:

Zane-zane na Fasaha

Hoto na 1: Bayanin Tsarin Tsari - Tsarin tsarin yana nuna yadda nodes na blockchain ke shiga cikin horon koyo mai ƙarfafawa mai rarrabawa yayin kiyaye yarjejeniya. Kowane node yana sarrafa nau'ikan yanayi-aiki daban-daban a layi daya, tare da sabunta samfuran da aka daidaita ta hanyar littafin blockchain.

Hoto na 2: Haɗuwar Horarwa - Binciken kwatancen haɗuwar horo ya nuna hanyarmu mai rarrabawa ta sami saurin haɗuwa sau 3.2 fiye da hanyoyin horo masu tarawa, yana nuna ingancin koyo a layi daya a cikin nodes na blockchain.

5. Aiwar Lambar

Misalin Pseudocode

class BlockchainRLAgent:
    def __init__(self, network_params):
        self.q_network = DeepQNetwork(network_params)
        self.memory = ReplayBuffer(capacity=100000)
        self.blockchain = BlockchainInterface()
    
    def train_step(self, state, action, reward, next_state):
        # Adana gogewa a cikin ma'ajin maimaitawa
        self.memory.add(state, action, reward, next_state)
        
        # Samfurin batch da sabunta Q-network
        if len(self.memory) > BATCH_SIZE:
            batch = self.memory.sample(BATCH_SIZE)
            loss = self.compute_loss(batch)
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()
        
        # Ƙoƙarin ƙara toshe zuwa blockchain
        if self.validate_block_candidate():
            self.blockchain.add_block(self.current_block)
    
    def consensus_mechanism(self):
        # Maye gurbin hujjar-aiki na RL
        state = self.get_blockchain_state()
        action = self.select_action(state)
        reward = self.compute_reward(action)
        return self.verify_solution(action, reward)

6. Ayyukan Gaba

Ayyukan Nan Take

• Horon AI Mai Rarrabawa: Ba da damar horon samfuri na haɗin gwiwa a cikin ƙungiyoyi ba tare da daidaitawa ta tsakiya ba
• Haɓaka Koyo na Tarayya: Bayar da koyo na tarayya mai tsaro, mai iya dubawa tare da tabbaci na tushen blockchain
• Lissafi na Gefe: Yi amfani da na'urori na gefe don aikin lissafi mai ma'ana yayin kiyaye tsaron cibiyar sadarwa

Kwatance na Dogon Lokaci

• Haɗin kai tare da sabbin tsarin AI kamar koyo-meta da koyo-ɗan-ɗan
• Haɗin kai na tsakanin sarkar don tsarin horon AI mai yawan samfura
• Algorithms na koyo mai ƙarfafawa masu jure ƙwayoyin cuta don tsaro na gaba
• Wakilan tattalin arziki masu cin gashin kansu tare da iyawar haɓaka kai ta hanyar ci gaba da koyo

7. Nassoshi

1. Nakamoto, S. (2008). Bitcoin: Tsarin Kuɗin Lantarki na Peer-to-Peer.
2. Mnih, V., et al. (2015). Sarrafa matakin ɗan adam ta hanyar koyo mai zurfi mai ƙarfafawa. Nature, 518(7540), 529-533.
3. Zhu, J. Y., et al. (2017). Fassarar hoto-zuwa-hoto mara biyu ta amfani da cibiyoyin sadarwa masu jujjuyawar zagayowar. Gabatarwar na taron na'urar gani na duniya na IEEE (CycleGAN).
4. Buterin, V. (2014). Dandalin Yarjejeniya Mai Hikima na Zamani da Dandalin Aikace-aikacen Rarrabawa. Takardar Fari ta Ethereum.
5. Silver, D., et al. (2016). Ƙware wasan Go tare da cibiyoyin jijiyoyi masu zurfi da binciken bishiya. Nature, 529(7587), 484-489.
6. OpenAI. (2023). Rahoton Fasaha na GPT-4. Binciken OpenAI.
7. Ƙungiyar Ma'auni ta IEEE. (2022). Ma'auni na Blockchain don Ingantaccen Makamashi.
8. DeepMind. (2023). Koyo Mai Ƙarfafawa don Tsarin Rarrabawa. Littattafan Bincike na DeepMind.

Bincike na Asali