<p>The agent technology represents one of the most consistent approaches to distributed artificial intelligence. Agents are characterized by autonomous, reactive, proactive, and social behavior.&nbsp;In addition, more complex, intelligent agents are often defined&nbsp;in terms of human-like mental attitudes, such as beliefs, desires,&nbsp;and intentions.</p><p>This thesis deals with software agents and multiagent systems&nbsp;in several ways. First, it defines a new reasoning architecture&nbsp;for intelligent agents called<em> Distributed Non-Axiomatic Reasoning System </em>(DNARS). Instead of the popular Belief-Intention-Desire model, it uses Non-Axiomatic Logic, a formalism developed for the domain of articial general intelligence. DNARS&nbsp;is highly-scalable, capable of answering questions and deriving&nbsp;new knowledge over large knowledge bases, while, at the same&nbsp;time, concurrently serving large numbers of external clients.&nbsp;</p><p>Secondly, the thesis proposes a novel agent runtime environment&nbsp;named<em> Siebog.</em> Based on the modern web and enterprise stan-dards, Siebog tries to reduce the gap between the agent technology and industrial applications. Like DNARS, Siebog is a&nbsp;distributed system. Its server side runs on computer clusters&nbsp;and provides advanced functionalities, such as automatic agent&nbsp;load-balancing and fault-tolerance. The client side, on the other<br />hand, runs inside web browsers, and supports a wide variety of&nbsp;hardware and software platforms.</p><p>Finally, Siebog depends on DNARS for deploying agents with&nbsp;unique reasoning capabilities.</p> / null / <p>Agentska tehnologija predstavlja dosledan pristup razvoju distribuirane ve&scaron;tačke&nbsp; inteligencije. Ono &scaron;to agente izdvaja od ostalih pristupa su autonomno, reaktivino,&nbsp; pro-aktivno, i socijalno pona&scaron;anje. Pored toga, kompleksniji, inteligentni agenti se često defini&scaron;u koristeći ljudske mentalne konstrukcije, kao sto su verovanja, želje i namere.</p><p>Disertacija se bavi softverskim agentima i multiagentskim sistemima sa nekoliko aspekata. Prvo, definisana je nova&nbsp; arhitektura za rasuđivanje sa primenom u razvoju&nbsp; inteligentnih agenata, nazvana Distribuirani sistem za ne-aksiomatsko rasuđivanje&nbsp; (eng. <em>Distributed Non-Axiomatic Reasoning System</em>) (DNARS). Umesto popularnog&nbsp;&nbsp; BDI modela za razvoj inteligentnih agenata (eng. <em>Belief-Desire-Intention</em>),&nbsp; arhitektura&nbsp; se zasniva na tzv. <em>ne-aksiomatskoj</em> logici, formalizmu razvijenom u domenu ve&scaron;tačke&nbsp; op&scaron;te inteligencije. DNARS je skalabilan softverski sistem, sposoban da odgovara na&nbsp;&nbsp; pitanja i da izvodi nove zaključke na osnovu veoma velikih&nbsp; baza znanja, služeći pri&nbsp;&nbsp; tome veliki broj klijenata.</p><p>Zatim, u disertaciji je predložena nova multiagentska platforma nazvana Siebog. Siebog je zasnovan na modernim standardima za razvoj veb aplikacija, čime poku&scaron;ava da smanji razliku izmedu multiagentskih sistema i sistema koji se koriste u&nbsp;industriji. Kao DNARS, i Siebog je distribuiran sistem. Na serverskoj strani, Siebog se izvr&scaron;ava na računarskim klasterima, pružajući napredne funkcionalnosti, poput automatske distribucije agenata i otpornosti na gre&scaron;ke. Sa klijentske strane, Siebog&nbsp;se izvr&scaron;ava u veb pretrazivačima i podržava &scaron;iroku lepezu hardverskih i softverskih platformi.</p><p>Konačno, Siebog se oslanja na DNARS za ravoj agenata sa jedinstvenim sposobnostima za rasuđivanje.</p>

This essay critically examines the limitations of artificial intelligence (AI) in achieving human-like understanding and intelligence. Despite significant advancements in AI, such as the development of sophisticated machine learning algorithms and neural networks, current systems fall short in comprehending the cognitive depth and flexibility inherent in human intelligence. Through an exploration of historical and contemporary arguments, including Searle's Chinese Room thought experiment and Dennett's Frame Problem, this essay highlights the inherent differences between human cognition and AI. Central to this analysis is the role of metaphorical thinking and embodied cognition, as articulated by Lakoff and Johnson, which are fundamental to human understanding but absent in AI. Proponents of AGI, like Kurzweil and Bostrom, argue for the potential of AI to surpass human intelligence through recursive self-improvement and technological integration. However, this essay contends that these approaches do not address the core issues of experiential knowledge and contextual awareness. By integrating insights from contemporary scholars like Bender, Koller, Buckner, Thorstad, and Hoffmann, the essay ultimately concludes that AI, while a powerful computational framework, is fundamentally incapaple of replicating the true intelligence and understanding unique to humans.