Russian Federation

Social virtual actors need to interact with users emotionally, convincing them in their ability to understand human minds. For this to happen, an artificial general emotional intelligence is needed, capable of believable behavior in real-life situations. Summarizing recent work of the authors, the present work extends the general state-of-the-art framework of emotional AGI, using the emotional Biologically Inspired Cognitive Architecture (eBICA) as a basis. In addition to appraisals, other kinds of fluents are added to the model, including somatic markers, feelings, emotional biases, moods, etc. Their integration is achieved based on semantic maps and moral schemas. It is anticipated that the proposed new level of artificial general socially emotional intelligence will complement the next-generation AGI, helping it to merge into our society on equal with its human members.

AGI-20

Toward a General Believable Model of Human-Analogous Intelligent Socially Emotional Behavior

semantic map

bica challenge

socially emotional intelligence

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

Underline hosted the two main days of AGI 2020 on Wednesday June 24th and Thursday June 25th. Both days were **live-streamed** and you can read the full program bellow. All presentations are available for a **asynchronous viewing**. All presenters are grouped into panels. You can also hear guest talks by **Ben Goertzel** (SingularityNET), **Valery Tarasov** (Semenov Institute of Chemical Physics), **Tatiana Shavrina** (Sberbank) and **Joscha Bach** (MIT Media Lab). 

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

The DeepWalk algorithm is used to assign embedding vec- tors to nodes in the Atomspace weighted, labeled hypergraph that is used to represent knowledge in the OpenCog AGI system, in the con- text of an application to probabilistic inference regarding the causes of longevity based on data from biological ontologies and genomic analyses. It is shown that vector difference operations between embedding vectors are, in appropriate conditions, approximately alignable with “intensional difference” operations between the hypergraph nodes corresponding to the embedding vectors. This relationship hints at a broader functorial mapping between uncertain intensional logic and vector arithmetic, and opens the door for using embedding vector algebra to guide intensional inference control.

Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences

What kind of programming language would be most appropriate to serve the needs of integrative, multi-paradigm, multi-software-system approaches to AGI? This question is broached via exploring the more particular question of how to create a more scalable and usable version of the ”Atomese” programming language that forms a key component of the OpenCog AGI design (an ”Atomese 2.0”) .

What Kind of Programming Language Best Suits Integrative AGI?

A novel computational model (CoDD) utilizing combinatory logic to create higher-order decision trees is presented. A theoretical analysis of general intelligence in terms of the formal theory of pattern recognition and pattern formation is outlined, and shown to take especially natural form in the case where patterns are expressed in CoDD language. Relationships between logical entropy and algorithmic information, and Shannon entropy and runtime complexity, are shown to be elucidated by this approach. Extension to the quantum computing case is also briefly discussed.

Combinatorial Decision Dags: A Natural Computational Model for General Intelligence

While several tools exist for training and evaluating narrow machine learning (ML) algorithms, their design generally does not follow a particular or explicit evaluation methodology or theory. Inversely so for more general learners, where many evaluation methodologies and frameworks have been suggested, but few specific tools exist. In this paper we introduce a new framework for broad evaluation of artificial intelligence (AI) learners, and a new tool that builds on this methodology. The platform, called SAGE (Simulator for Autonomy \& Generality Evaluation), works for training and evaluation of a broad range of systems and allows detailed comparison between narrow and general ML and AI. It provides a variety of tuning and task construction options, allowing isolation of single parameters across complexity dimensions. SAGE is aimed at helping AI researchers map out and compare strengths and weaknesses of divergent approaches. Our hope is that it can help deepen understanding of the various tasks we want AI systems to do and the relationship between their composition, complexity, and difficulty for various AI systems, as well as contribute to building a clearer research road map for the field. This paper provides an overview of the framework and presents results of an early use case.

SAGE: Task-Environment Platform for Evaluating a Broad Range of AI Learners

We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an ``AI kernel'' for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages, which are briefly introduced. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, and intelligent agents.

Omega: An Architecture for AI Unification

From Psychonics to Artificial General Intelligence and Hybrid Intelligence

Texts as an unlimited, but indirect trace of human thinking processes are the richest source for AI modelling that we have. How to handle it and where does SuperGLUE come about - let's find out together.

Russian SuperGLUE Creating a Language Understanding Evaluation Benchmark

Panel discussion with authors of papers about 'Cognitive Architectures and Agents' at AGI-20 conference. Held live on Underline, 06/24/2020

Panel Session 1: Cognitive Architectures and Agents

Panel discussion with authors of papers about 'Intelligence measures, AGI Benchmarks and Evaluations' at AGI-20 conference. Held live on Underline, 06/24/2020

Panel Session 2: Intelligence measures, AGI Benchmarks and Evaluations

Panel discussion with authors of papers about 'Learning, Reasoning, and Resource Allocation' at AGI-20 conference. Held live on Underline, 06/24/2020

Downloads

Next from AGI-20

Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences

Similar lecture

Improving Dialogue Intent Classification with a Knowledge-Enhanced MultifactorGraph Model

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AGI-20

Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences

Similar lecture

Improving Dialogue Intent Classification with a Knowledge-Enhanced MultifactorGraph Model

Downloads