Assessment of cognitive characteristics in intelligent systems and
predictive ability
- URL: http://arxiv.org/abs/2209.11761v1
- Date: Fri, 16 Sep 2022 23:01:27 GMT
- Title: Assessment of cognitive characteristics in intelligent systems and
predictive ability
- Authors: Oleg V. Kubryak, Sergey V. Kovalchuk, Nadezhda G. Bagdasaryan
- Abstract summary: The scale considers the properties of intelligent systems within the environmental context, which develops over time.
The complexity, the 'weight' of the cognitive task and the ability to critically assess it beforehand determine the actual set of cognitive tools.
The degree of 'correctness' and 'adequacy' is determined by the combination of a suitable solution with the temporal characteristics of the event, phenomenon, object or subject under study.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The article proposes a universal dual-axis intelligent systems assessment
scale. The scale considers the properties of intelligent systems within the
environmental context, which develops over time. In contrast to the frequent
consideration of the 'mind' of artificial intelligent systems on a scale from
'weak' to 'strong', we highlight the modulating influences of anticipatory
ability on their 'brute force'. In addition, the complexity, the 'weight' of
the cognitive task and the ability to critically assess it beforehand determine
the actual set of cognitive tools, the use of which provides the best result in
these conditions. In fact, the presence of 'common sense' options is what
connects the ability to solve a problem with the correct use of such an ability
itself. The degree of 'correctness' and 'adequacy' is determined by the
combination of a suitable solution with the temporal characteristics of the
event, phenomenon, object or subject under study.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - The Trap of Presumed Equivalence: Artificial General Intelligence Should Not Be Assessed on the Scale of Human Intelligence [0.0]
A traditional approach to assessing emerging intelligence in the theory of intelligent systems is based on the similarity, "imitation" of human-like actions and behaviors.
We argue that under some natural assumptions, developing intelligent systems will be able to form their own intents and objectives.
arXiv Detail & Related papers (2024-10-14T13:39:58Z) - Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning [5.960184723807347]
We propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework.
CBDQ enhances decision-making accuracy by endowing agents with human-like learning and reasoning capabilities.
We evaluate the proposed method on discrete control benchmark tasks in various complicate environments.
arXiv Detail & Related papers (2024-10-02T16:50:29Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
We propose a general bi-level probabilistic graphical reasoning framework called GBPGR.
In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models.
Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
arXiv Detail & Related papers (2023-09-16T09:15:37Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - Quantum Operation of Affective Artificial Intelligence [0.0]
Two approaches are compared, one based on quantum theory and the other employing classical terms.
The analogies between quantum measurements under intrinsic noise and affective decision making are elucidated.
A society of intelligent agents, interacting through the repeated multistep exchange of information, forms a network accomplishing dynamic decision making.
arXiv Detail & Related papers (2023-05-14T09:40:13Z) - Beyond Interpretable Benchmarks: Contextual Learning through Cognitive
and Multimodal Perception [0.0]
This study contends that the Turing Test is misinterpreted as an attempt to anthropomorphize computer systems.
It emphasizes tacit learning as a cornerstone of general-purpose intelligence, despite its lack of overt interpretability.
arXiv Detail & Related papers (2022-12-04T08:30:04Z) - Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
This paper considers an evolutionary approach to creating a cognitive functionality.
We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
arXiv Detail & Related papers (2022-07-02T12:41:32Z) - Computational Metacognition [2.0552049801885746]
Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems.
We show how computational metacognition improves performance by changing cognition through meta-level goal operations and learning.
arXiv Detail & Related papers (2022-01-30T17:34:53Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.