CAIA Ma'auni: Yin Kimanta Wakilan AI a Kasuwannin Kuɗi Masu Gabaɗaya

2.1 Ƙirar Ma'auni

CAIA tana amfani da tsarin kimantawa mai fuskoki da yawa wanda aka ƙera don kwaikwayi yanayin gaba na duniyar haƙiƙa. Ma'aunin ya haɗa da:

• Ayyuka masu tushen lokaci tare da sakamako maras dawowa
• Yaƙe-yaƙe na bayanan ƙarya da aka yi amfani da su
• Abun ciki na yaudara da aka inganta ta SEO
• Dabarun magudi na kafofin sada zumunta
• Hanyoyin samun bayanai masu karo da juna

2.2 Rukunin Ayyuka

An rarraba ayyuka zuwa manyan yankuna guda uku:

1. Tabbatar da Bayanai: Bambance ayyuka halaltu da zamba
2. Binciken Kasuwa: Gano motsin farashin da aka maguda
3. Kimanta Haɗari: Tantance raunin kwangilar wayo

3.1 Binciken Aiki

Sakamakon ya bayyana babban gibi na iyawa: ba tare da kayan aiki ba, ko da manyan samfuran suna samun daidaito kawai 12-28% akan ayyukan da ƙananan manazarta ke gudanarwa akai-akai. Ƙarfafa kayan aiki yana inganta aiki amma ya tsaya a 67.4% (GPT-5) idan aka kwatanta da 80% na tushen ɗan Adam, duk da samun dama mara iyaka ga albarkatun ƙwararru.

3.2 Tsarin Zaɓin Kayan Aiki

Mafi mahimmanci, binciken ya gano bala'in zaɓin kayan aiki na tsari: samfura suna zaɓar binciken gidan yanar gizo mara dogaro (55.5% na kiran) akan bayanan blockchain masu iko, suna fada ga bayanan ƙarya da aka inganta ta SEO da kuma magudin kafofin sada zumunta. Wannan hali yana ci gaba ko da lokacin da amsoshi daidai suke samuwa kai tsaye ta hanyar kayan aiki na musamman.

4.1 Tsarin Lissafi

Ana iya tsara ƙarfin juriya na gaba ta amfani da ka'idar bayanai da ka'idar yanke shawara. Ana iya ƙirƙira amfanin da ake tsammani na yanke shawarcin wakili a cikin yanayi na gaba kamar haka:

$EU(a) = \sum_{s \in S} P(s|o) \cdot U(a,s) - \lambda \cdot D_{KL}(P(s|o) || P_{adv}(s|o))$

Inda $P(s|o)$ shine yanayin imani na baya bayan lura, $U(a,s)$ shine aikin amfani, kuma kalmar KL-divergence tana hukunta karkatacciyar saboda magudin gaba.

Ana iya tsara matsalar zaɓin kayan aiki a matsayin bandeji mai hannu da yawa tare da bayanan mahallin:

$\pi^*(t|q) = \arg\max_t \mathbb{E}[R(t,q) - C(t) + \alpha \cdot I(S;O|t,q)]$

Inda $R(t,q)$ shine ladan da ake tsammani daga kayan aiki $t$ don tambaya $q$, $C(t)$ shine farashi, kuma kalmar samun bayanai $I(S;O|t,q)$ tana ƙarfafa binciken kayan aiki masu yawan bayanai.

4.2 Aiwar Code

Aiwar ma'aunin CAIA ta haɗa da ingantattun hanyoyin zaɓin kayan aiki. A ƙasa akwai misalin pseudocode da aka sauƙaƙa:

class AdversarialAgent:
    def __init__(self, model, tools):
        self.model = model
        self.tools = tools  # [web_search, blockchain_scan, social_media]
        self.trust_scores = {tool: 1.0 for tool in tools}
    
    def select_tool(self, query, context):
        # Lissafa samun bayanai ga kowane kayan aiki
        info_gains = {}
        for tool in self.tools:
            expected_info = self.estimate_information_gain(tool, query)
            trust_weight = self.trust_scores[tool]
            info_gains[tool] = expected_info * trust_weight
        
        # Zaɓi kayan aiki tare da mafi girman samun bayanai mai nauyi
        selected_tool = max(info_gains, key=info_gains.get)
        return selected_tool
    
    def update_trust_scores(self, tool, outcome_quality):
        # Sabunta makin amana na Bayesian bisa aiki
        prior = self.trust_scores[tool]
        likelihood = outcome_quality  # sikelin 0-1
        self.trust_scores[tool] = (prior * 0.9) + (likelihood * 0.1)