[agents] Call for Papers on "The Way Forward with AI-complete Problems"

[Sven Groppe]

Call for Papers on "The Way Forward with AI-complete Problems"
A special issue of New Generation Computing solicits papers on the theme of AI-complete Problems.

Paper Submission Deadline: September 30, 2022

Publication Date: Accepted papers will be made available online on the Springer website shortly after acceptance. The publication date of the printed version will be determined when the Special Issue is finalized.

Paper Submission Format: Refer to the journal home page (https://www.springer.com/journal/354/submission-guidelines). All papers must be submitted to the journal's submission system (https://www.editorialmanager.com/ngco/default.aspx).

During the submission stage, please select “Yes” for the question “Does this manuscript belong to a special feature?” in the Additional Information tab, then select the special feature “S.I.: AI-complete Problems”.

Scope of This Issue
This call is open to all.

Intelligent agents are "Intelligent" pieces of software that can work autonomously, seek necessary/present/relevant/authentic information, coordinate with each other, understand the contents, and take necessary actions to make life simple for human beings. There are three information aspects for an intelligent agent: syntax (sentence construction, grammatical correctness), semantics (human-level interaction), and pragmatics (intention behind the communication. An intelligent agent is required to fuse heterogeneous sources of information together for which it should be equipped with both the data-driven (statistical) and knowledge-driven (symbolic) AI disciplines. We need a representation of our data that not only includes the data itself but where the interactions in it is a first-class citizen.

We have seen in the past decade that statistical models have revolutionized the world. Though the Statistical models have already proved themselves, they are not a Universal Solvent but only a tool as others. Deep learning is very good at learning in a static world and executing low-level patterns, provided it is fed with a lot of data. More deep, more intelligent, and of course more black. This is the crux of the problem that this special issue will emphasize. The question is “Is the AI of today Artificial Super Intelligence (ASI) / Artificial General Intelligence (AGI) / Artificial Narrow Intelligence (ANI)? Is the AI of today the AI that we are craving for?” In fact, today’s artificial intelligence is weak AI. There are a number of instances where DL has produced delusional and unrealistic results. Accuracy alone is not sufficient. We require exploring ways of opening the black box of statistical models. When DL researchers are asked to open the black box, this today implies less intelligent models to them (limited capability). In addition to increased performance, AGI aims to build trust.

Symbolic AI and statistical AI have to go together to achieve contextual computing. The symbolist approach is nowadays manifested as a knowledge graph that advanced statistics and machine learning can run on top of. The Hybrid Model combines machine intelligence with human intelligence to reach conclusions faster than possible by humans alone along with the explanations needed for trust in the decisions and results; while requiring far fewer data samples for training and conversing in natural language. The Hybrid Model is able to generalize and is excellent at perceiving, learning, and reasoning with minimal supervision. In addition, semantics have come a long way in enhancing explainability in AI systems.

Keywords: Hybrid of Statistical and Symbolic Approach, Knowledge Graph, Contextual Computing, Artificial General Intelligence, Explainable AI, AI-complete

Topics: This special issue addresses three types of manuscripts. Type I focusing on the perspectives and survey of existing scenarios, Type II receiving the application-oriented submissions, and Type III consisting of research results in specified topics. The topics of interest include topics pertaining to semantic computing as well as the hybrid approach. Here is a tentative list:

Representation, Reasoning, and Learning
Bridging the Neuro-Symbolic Gap
Knowledge Graph Embeddings
Knowledge Organization Systems
Contextualized Word Embeddings
Ontology-based Classification
Context Understanding
Proposing new approaches for making the hybrid possible
Knowledge Matching and Ontology Matching
Natural Language Understanding
Neuro-Symbolic Architectures
Explainable AI and Evaluating Explanations

Guest Editors:
Sven Groppe, University of Lübeck, Germany
Sarika Jain, National Institute of Technology, Kurukshetra, Haryana, India