Comparing NARS and Reinforcement Learning: An Analysis of ONA and QQ-Learning Algorithms

In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS), which is a general-purpose cognitive reasoning framework. In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks. To investigate this, we conduct a comparative analysis of the performance of ONA as an implementation of NARS and $Q$-Learning in various environments that were created using the Open AI gym. The environments have different difficulty levels, ranging from simple to complex. Our results demonstrate that NARS is a promising alternative to RL, with competitive performance in diverse environments, particularly in non-deterministic ones.Comment: Accepted in the 16th AGI Conference (AGI-23), Stockholm, Sweden, June 16 - June 19, 2023. arXiv admin note: text overlap with arXiv:2212.1251

Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity

Traditional computer vision models often require extensive manual effort for data acquisition, annotation and validation, particularly when detecting subtle behavioral nuances or events. The difficulty in distinguishing routine behaviors from potential risks in real-world applications, such as differentiating routine shopping from potential shoplifting, further complicates the process. Moreover, these models may demonstrate high false positive rates and imprecise event detection when exposed to real-world scenarios that differ significantly from the conditions of the training data. To overcome these hurdles, we present Ethosight, a novel zero-shot computer vision system. Ethosight initiates with a clean slate based on user requirements and semantic knowledge of interest. Using localized label affinity calculations and a reasoning-guided iterative learning loop, Ethosight infers scene details and iteratively refines the label set. Reasoning mechanisms can be derived from large language models like GPT4, symbolic reasoners like OpenNARS\cite{wang2013}\cite{wang2006}, or hybrid systems. Our evaluations demonstrate Ethosight's efficacy across 40 complex use cases, spanning domains such as health, safety, and security. Detailed results and case studies within the main body of this paper and an appendix underscore a promising trajectory towards enhancing the adaptability and resilience of computer vision models in detecting and extracting subtle and nuanced behaviors

Разрешавање идентитета и груписање дигиталних доказа о осумњиченима применом технологија препознавања лица и система софтверских интелигентих агената заснованог на неаксиоматском резоновању

The work of criminal police in modern society is characterized by the proliferation of data and information to be processed, greater demands for restrictions on personal data, increased public monitoring, and higher expectations in the efficiency of detecting perpetrators, but still lack resources, both human and material. One of the more complex tasks is to resolve the identity, the change of which seeks to cover up criminal activities, i.e., the perpetrator himself, who is on the run. In order to resolve the identity, it is necessary to group and present all available evidence related to specific persons. The thesis proposes a clustering approach by comparing pairs of face feature vectors extracted from images created in unconstrained conditions and based on reasoning using non-axiomatic logic and graphs. Face clusters will be the central points around which data from various police reports will be grouped. A system model has also been proposed in which software agents will play a significant role, primarily in connecting the distribution environment points formed in practice by police information systems. The clustering approach was experimentally tested with six different face image databases characterized by the fact that they were created in a way that simulates unconstrained conditions. The obtained results of the proposed solution are compared with other state-of-the-art methods. The results showed that the approach gives similar but mostly better results than the others. What gives a notable advantage over other methods is the possibility of using mechanisms from non-axiomatic logic such as revision and deduction, which can be used to acquire new knowledge based on information from different system nodes, or in the local knowledge base, respectively.Рад криминалистичке полиције у савременом друштву одликује пролиферација података и информација које треба обрађивати, већи захтеви за ограничењима у раду са личним подацима, појачани надзор пре свега јавности, већа очекивања у ефикасности откривања извршилаца кривичних дела, али и даље недостатак ресурса, како људских тако и материјалних. Један од сложенијих задатака јесте разрешавање идентитета чијом променом се настоје прикрити криминалне активности, односно сам извршилац који је у бекству. Да би се разрешио идентитет, потребно је груписати и презентовати све расположиве доказе везане за одређене особе. У дисертацији је предложен нови приступ кластеровању поређењем парова вектора одлика лица екстрахованих из слика насталих у неконтролисаним условима, а заснован на резоновању применом неаксиоматске логике и графова. Кластери слика лица представљају централне тачке око којих се групишу подаци из различитих полицијских извештаја. Такође је предложен модел система у коме ће значајну улогу имати софтверски агенти, пре свега у повезивању тачака дистрибуираног окружења које у пракси формирају полицијски информациони системи. Нови приступ кластеровању је експериментално испитан са шест различитих база података лица карактеристичних по томе што су креиране на начин којим се симулирају неконтролисани услови. Добијени резултати предложеног решења су упоређени са осталим врхунским методама. Резултати су показали да приступ даје приближне, али углавном боље резултате од осталих. Оно што даје посебну предност у односу на остале методе јесте могућност коришћења механизама из неаксиоматске логике попут ревизије и дедукције, помоћу којих се могу стицати нова знања на основу информација из различитих нодова система, или у локалној бази знања, респективно