<abstract><title style='display:none'>Abstract</title>This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.</abstract>

On Defining Artificial Intelligence

<abstract><title style='display:none'>Abstract</title> In recent years broad community of researchers has emerged, focusing on the original ambitious goals of the AI field - the creation and study of software or hardware systems with general intelligence comparable to, and ultimately perhaps greater than, that of human beings. This paper surveys this diverse community and its progress. Approaches to defining the concept of Artificial General Intelligence (AGI) are reviewed including mathematical formalisms, engineering, and biology inspired perspectives. The spectrum of designs for AGI systems includes systems with symbolic, emergentist, hybrid and universalist characteristics. Metrics for general intelligence are evaluated, with a conclusion that, although metrics for assessing the achievement of human-level AGI may be relatively straightforward (e.g. the Turing Test, or a robot that can graduate from elementary school or university), metrics for assessing partial progress remain more controversial and problematic.</abstract>

Artificial General Intelligence: Concept, State of the Art, and Future Prospects

<abstract xml:lang="en"><title style='display:none'>Editorial: Cognitive Architectures, Model Comparison and AGI</title>Cognitive Science and Artificial Intelligence share compatible goals of understanding and possibly generating broadly intelligent behavior. In order to determine if progress is made, it is essential to be able to evaluate the behavior of complex computational models, especially those built on general cognitive architectures, and compare it to benchmarks of intelligent behavior such as human performance. Significant methodological challenges arise, however, when trying to extend approaches used to compare model and human performance from tightly controlled laboratory tasks to complex tasks involving more open-ended behavior. This paper describes a model comparison challenge built around a dynamic control task, the Dynamic Stocks and Flows. We present and discuss distinct approaches to evaluating performance and comparing models. Lessons drawn from this challenge are discussed in light of the challenge of using cognitive architectures to achieve Artificial General Intelligence.</abstract>

Psychology Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

Dynamic Decision Making Laboratory, Social and Decision Sciences Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

Alion Science and Technology, 4949 Pearl East Circle, Suite 200, Boulder, CO 80301, USA

Editorial: Cognitive Architectures, Model Comparison and AGI

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

<abstract><title style='display:none'>Abstract</title>Sigma (Σ) is a cognitive architecture and system whose development is driven by a combination of four desiderata: <italic>grand unification</italic>, <italic>generic cognition</italic>, <italic>functional elegance</italic>, and <italic>sufficient efficiency</italic>. Work towards these desiderata is guided by the <italic>graphical architecture hypothesis</italic>, that key to progress on them is combining what has been learned from over three decades’ worth of separate work on <italic>cognitive architectures</italic> and <italic>graphical models</italic>. In this article, these four desiderata are motivated and explained, and then combined with the graphical architecture hypothesis to yield a rationale for the development of Sigma. The current state of the cognitive architecture is then introduced in detail, along with the graphical architecture that sits below it and implements it. Progress in extending Sigma beyond these architectures and towards a full cognitive system is then detailed in terms of both a systematic set of higher level <italic>cognitive idioms</italic> that have been developed and several <italic>virtual humans</italic> that are built from combinations of these idioms. Sigma as a whole is then analyzed in terms of how well the progress to date satisfies the desiderata. This article thus provides the first full motivation, presentation and analysis of Sigma, along with a diversity of more specific results that have been generated during its development.</abstract>

Institute for Creative Technologies and Department of Computer Science, 12015 Waterfront Dr. Playa Vista, CA 90094,

Institute for Creative Technologies, 12015 Waterfront Dr. Playa Vista, CA 90094,

The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification

Carboncopies.org 1087 Mission Street San Francisco, CA 94103, USA

Institute of Neuroscience Biedersteiner Str. 29 D-80802 Mnchen, Germany

Editorial: Whole Brain Emulation seeks to Implement a Mind and its General Intelligence through System Identification

<abstract>
<title style='display:none'>Abstract</title>
There has been considerable recent interest in the “The Reward is Enough” hypothesis, which is the idea that agents can develop general intelligence even with simple reward functions, provided the environment they operate in is sufficiently complex. While this is an interesting framework to approach the AGI problem, it also brings forth new questions - what kind of RL algorithm should the agent use? What should the reward function look like? How can it quickly generalize its learning to new tasks? This paper looks to animal reinforcement learning - both individual and social - to address these questions and more. It evaluates existing computational models and neural substrates of Pavlovian conditioning, reward-based action selection, intrinsic motivation, attention-based task representations, social learning and meta-learning in animals and discusses how insights from these findings can influence the development of animal-level AGI within an RL framework.
</abstract>

What’s Next if Reward is Enough? Insights for AGI from Animal Reinforcement Learning

<abstract>
<title style='display:none'>Abstract</title>
Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation <italic>C</italic>(<italic>a</italic>) for some concept <italic>C</italic>” or “what are the roles <italic>R</italic> that connect the individuals <italic>a</italic> and <italic>b</italic> to degree of sharedness ε.”
</abstract>

Fuzzy Networks for Modeling Shared Semantic Knowledge

<abstract>
<title style='display:none'>Abstract</title>
We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent’s hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment’s outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the agent to self-reflect. Thus weighted-average performance over the space of all suitably well-behaved extended environments could be considered a way of measuring how self-reflective an agent is. We give examples of extended environments and introduce a simple transformation which experimentally seems to increase some standard RL agents’ performance in a certain type of extended environment.
</abstract>

Extending Environments to Measure Self-reflection in Reinforcement Learning

<abstract>
<title style='display:none'>Abstract</title>
On the basis of the author’s earlier works, the article proposes a new approach to creating an artificial intellect system in a model of a human being that is presented as the unification of an intellectual agent and a humanoid robot (ARb). In accordance with the proposed new approach, the development of an artificial intellect is achieved by teaching a natural language to an ARb, and by its utilization for communication with ARbs and humans, as well as for reflections. A method is proposed for the implementation of the approach. Within the framework of that method, a human model is “brought up” like a child, in a collective of automatons and children, whereupon an ARb must master a natural language and reflection, and possess self-awareness. Agent robots (ARbs) propagate and their population evolves; that is ARbs develop cognitively from generation to generation. ARbs must perform the tasks they were given, such as computing, whereupon they are then assigned time for “private life” for improving their education as well as for searching for partners for propagation. After having received an education, every agent robot may be viewed as a “person” who is capable of activities that contain elements of creativity. The development of ARbs thanks to the evolution of their population, education, and personal “life” experience, including “work” experience, which is mastered in a collective of humans and automatons.
</abstract>

A New Approach to Creation of an Artificial Intellect and Method of its Implementation

<abstract>
<title style='display:none'>Abstract</title>
The Feature Markov Decision Processes ( MDPs) model developed in Part I (Hutter, 2009b) is well-suited for learning agents in general environments. Nevertheless, unstructured (Φ)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems. In this article I extend ΦMDP to ΦDBN. The primary contribution is to derive a cost criterion that allows to automatically extract the most relevant features from the environment, leading to the “best” DBN representation. I discuss all building blocks required for a complete general learning algorithm, and compare the novel ΦDBN model to the prevalent POMDP approach.
</abstract>

Feature Reinforcement Learning: Part II. Structured MDPs

<abstract>
<title style='display:none'>Abstract</title>
Thinking machines must be able to use language effectively in communication with humans. It requires from them the ability to generate meaning and transfer this meaning to a communicating partner. Machines must also be able to decode meaning communicated via language. This work is about meaning in the context of building an artificial general intelligent system. It starts with an analysis of the Turing test and some of the main approaches to explain meaning. It then considers the generation of meaning in the human mind and argues that meaning has a dual nature. The quantum component reflects the relationships between objects and the orthogonal quale component the value of these relationships to the self. Both components are necessary, simultaneously, for meaning to exist. This parallel existence permits the formulation of ‘meaning coordinates’ as ordered pairs of quantum and quale strengths. Meaning coordinates represent the contents of meaningful mental states. Spurred by a currently salient meaningful mental state in the speaker, language is used to induce a meaningful mental state in the hearer. Therefore, thinking machines must be able to produce and respond to meaningful mental states in ways similar to their functioning in humans. It is explained how quanta and qualia arise, how they generate meaningful mental states, how these states propagate to produce thought, how they are communicated and interpreted, and how they can be simulated to create thinking machines.
</abstract>

The Synthesis and Decoding of Meaning

<abstract xml:lang="en"><title style='display:none'>Abstract</title>One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.</abstract>

Center for Analysis & Design of Intelligent Agents, School of Computer Science Venus, 2nd fl. Reykjavik University Menntavegur 1, 101 Reykjavik, Iceland

Icelandic Institute for Intelligent Machines, 2. h. Uranus Menntavegur 1, 101 Reykjavik, Iceland

Cognitive Architectures and Autonomy: A Comparative Review

<abstract><title style='display:none'>Abstract</title> Significant debate on fundamental issues remains in the subfields of cognitive science, including perception, memory, attention, action selection, learning, and others. Psychology, neuroscience, and artificial intelligence each contribute alternative and sometimes conflicting perspectives on the supervening problem of artificial general intelligence (AGI). Current efforts toward a broad-based, systems-level model of minds cannot await theoretical convergence in each of the relevant subfields. Such work therefore requires the formulation of tentative hypotheses, based on current knowledge, that serve to connect cognitive functions into a theoretical framework for the study of the mind. We term such hypotheses “conceptual commitments” and describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model. Our intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents.</abstract>

Fedex Institute of Technology 301, The University of Memphis, TN 38152, USA

6615 Fisher Ave. Falls Church, VA 22046, USA

Conceptual Commitments of the LIDA Model of Cognition

Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

 Journal of Artificial General Intelligence (JAGI) is a peer-reviewed open-access academic journal, owned by the Artificial General Intelligence Society (AGIS).  Artificial...

 Journal of Artificial General Intelligence (JAGI) is a peer-reviewed open-access academic journal, owned by the Artificial General...

AHEAD OF PRINT

Volume 14 (2023): Issue 1 (March 2023)

Volume 13 (2022): Issue 1 (October 2022)

Volume 12 (2021): Issue 1 (January 2021)

Volume 11 (2020): Issue 2 (February 2020) Special Issue “On Defining Artificial Intelligence” — Commentaries and Author’s Response

Volume 11 (2020): Issue 1 (January 2020)

Volume 10 (2019): Issue 2 (January 2019)

Volume 10 (2019): Issue 1 (January 2019)

Volume 9 (2018): Issue 1 (March 2018)

Volume 8 (2017): Issue 1 (December 2017)

Volume 7 (2016): Issue 1 (December 2016)

Volume 6 (2015): Issue 1 (December 2015)

Volume 5 (2014): Issue 1 (December 2014)

Volume 4 (2013): Issue 3 (December 2013) Brain Emulation and Connectomics: a Convergence of Neuroscience and Artificial General Intelligence, Editors: Randal Koene and Diana Deca

Volume 4 (2013): Issue 2 (December 2013) Conceptual Commitments of AGI Systems, Editors:	Haris Dindo, James Marshall, and Giovanni Pezzulo

Volume 4 (2013): Issue 1 (November 2013)

Volume 3 (2012): Issue 3 (December 2012) Self-Programming and Constructivist Methodologies for AGI,
 Editors: Kristinn R. Thórisson, Eric Nivel and Ricardo Sanz

Volume 3 (2012): Issue 2 (June 2012)

Volume 3 (2012): Issue 1 (May 2012)

Volume 2 (2010): Issue 2 (December 2010) Cognitive Architectures, Model Comparison, and AGI, Editors: Christian Lebiere, Cleotilde Gonzalez and Walter Warwick