Technical Goals for the BICA Community Mark R. Waser

Goal creating a computational equivalent of the natural mind in its higher cognitive abilities Specific topics include cognitive architectures inspired by the brain, constraints borrowed from biology, human-like learning and self-sustained cognitive growth, self-regulated learning assistance, natural language acquisition, emotional and social intelligence, metrics and a roadmap to solving the challenge.

Goal creating a real-life computational equivalent of the human mind Specific topics include Bridging the gap between AI and biology: robustness, flexibility, integrity BICA models of learning: bootstrapped, self-regulated (SRL), meta-learning Scalability, limitations and ‘critical mass’ of human-like learning Biological constraints vital for learning Physical support of conscious experience Formal theory of cognitive architectures Emotional feelings and values in artifacts Measuring minds of machines and humans

Subgoals – 2008 & 2009 Part I. cognitive architectures inspired by the brain Formal theory of cognitive architectures constraints borrowed from biology Biological constraints vital for learning human-like learning and self-sustained cognitive growth self-regulated learning assistance BICA models of learning: bootstrapped, self-regulated (SRL), meta-learning natural language acquisition (NONE)

Subgoals – 2008 & 2009 Part II. emotional and social intelligence Physical support of conscious experience Emotional feelings and values in artifacts metrics Measuring minds of machines and humans a roadmap to solving the challenge Bridging the gap between AI & biology: robustness, flexibility, integrity Scalability, limitations and ‘critical mass’ of human-like learning

Goal creating a real-life computational equivalent of the human mind four schools of thought: (1)computational neuroscience, that tries to understand how the brain works in terms of connectionist models; (2)cognitive modeling, pursuing higher-level computational description of human cognition; (3)human-level artificial intelligence, aiming at generally intelligent artifacts that can replace humans at work; and (4)human-like learners: artificial minds that can be understood by humans intuitively, that can learn like humans, from humans and for human needs.

Subgoals – Part I. computational neuroscience (connectionist modeling) cognitive architectures (low-level) biological constraints (low-level) ??? cognitive modeling cognitive architectures (high-level) biological constraints (high-level) human-level artificial intelligence (that can replace humans at work) human-like learners/human-like artificial minds human-like learning natural language acquisition emotional and social intelligence

Subgoals – Part II. metrics a roadmap to solving the challenge creating a computational equivalent of the natural mind in its higher cognitive abilities creating a real-life computational equivalent of the human mind (human-level AGI) (human-like AGI+) safety!

Toward a Comparative Repository of Cognitive Architectures, Models, Tasks and Data Introduction (discussion panel agenda - by Christian Lebiere) First Step: Comparative Table of Cognitive Architectures Current comparative table: HTML | XLS | PDFHTMLXLSPDF Old comparative table - from Pew & Mavor, 1998 Complementary Frameworks for Comparison (4) Related Sites (3)

What Is Our Goal? A united working community dedicated to a specific common goal (2008 or 2010?) – OR – What Do We Want To Be? A social networking community dedicated to sharing/collecting information and recruiting

Thursday, November 5, 4:00 pm – 5:45 pm, Westin Arlington Gateway Hotel 1 AAAI 2009 Fall Symposium Series Arlington, Virginia – November 5‐7, 2009 Panel Discussion: Comparative Repository of Architectures, Models, Tasks and Data Chair: Christian Lebiere

Objective To identify the necessary means to achieve greater rates of convergence and incremental progress in cognitive modeling through the use of a shared repository of computational cognitive architectures, models, tasks and data.

Why do we need a repository? 2. To provide a centralized resource, that modelers, students, and teachers can access when they want to start a modeling research project. 3. To have an immediate and organized way to access an overview of relevant information. 4. To enable the reuse of models. 5. To encourage the development of modeling tools and standards. How are we going to spread it? 1. To facilitate direct comparison of different architectures.

How are we going to make it work? Uploading tasks and code as currently existing is not enough. The following issues should be considered. 1. A standard API between cognitive architectures and task simulation environments is needed to assure portability across tasks and models. 2. Models need to be updated and kept current. 3. Infrastructure funding should be provided by some source, 4. Before proceeding with the implementation, some informal polls or surveys should be taken to study the modelers’ habits and needs