Biologically Inspired Cognitive Architectures (BICAs) are computational frameworks for building intelligent agents that are inspired by biological intelligence. These agents serve both as theoretical models (e.g., in cognitive science, neuroscience, economics and social sciences), and as intelligent controllers for autonomous systems (robots, games characters, smart human/machine interfaces, health applications, etc).

Biological intelligent systems (animals, including humans) have many qualities that are often lacking in artificially designed systems; their purpose goes beyond interacting with a closed environment or solving predefined logical problems. At the time when our understanding of natural intelligence is exploding, thanks to modern brain imaging, ethological studies, and the development of cognitive models mapping brain structures with functions, our ability to learn lessons from nature and to build biologically inspired intelligent systems has never been greater. At the same time, the growth in computer science and technology has unleashed enough design creativity and computational power to generate an explosion of applications in multiple domains.

Research in Biologically Inspired Cognitive Architectures contributes to the development of these applications by addressing the numerous questions raised by the problem of replicating natural intelligence – specifically, the complexity of higher cognitive abilities of the human mind – in an artificial system (widely known as the BICA Challenge). These questions are trans-disciplinary in nature and promise to yield multi-directional flow of understanding between all the involved disciplines.

Scope

With the scope of BICA 2015 covering all areas of BICA research listed below, this year's major thrust will be on learning from experience of sensorimotor interaction. Here, the key questions are:

Learning: how a system that has no direct ontological access to reality can construct knowledge about reality based on regularities of interaction?
Self-motivation: what key motivational drives (e.g., emotions, behavioral preferences, social interactions) should we incorporate in models of self-motivated cognitive systems?
Methodology: how to assess active open-ended learning? What methods can we draw from biology to define and assess intelligent behaviors beyond pre-defined tasks and pre-modeled problems?
Models of interaction with the environment: can we define models alternative to the traditional perception-cognition-action cycle?
What emergent mathematical foundations can support sensorimotor and other forms of learning?

In addition to these focus topic areas of BICA 2015, we encourage submission of papers in all areas relevant to BICA research, especially in the following areas:

Neuroscience:

“B” in BICA: useful biological constraints for cognitive architectures
Bridging the gap between artificial and natural information processing
Cognitive and learning mechanisms informed by neuroscience
Neural correlates of cognitive and meta-cognitive processes
Robustness, scalability and adaptability in neuromorphic systems
Neurophysiological underpinnings of reinforcement learning
Physiological mechanisms of memory formation and (re)consolidation
Representation of contextual and conceptual knowledge in neural systems

Social, Economic and Educational Sciences:

Mixed-initiative systems based on inspirations from biology
Agents possessing human-level social, narrative, and emotional intelligence
BICA in pedagogical, learning, and tutoring technologies and education
BICA models of self and their application to self-aware perception and action
Representation, perception, understanding, processing and expression of emotions
Virtual characters and narratives, artificial personalities and human-compatibility of BICA
Agent-based modeling of intelligent social phenomena (are there any?)
Applications of BICA technologies in elderly care

Cognitive Science:

Perception, reasoning, decision making and action in BICA
Combining natural and artificial approaches to cognition
Comparison of different forms of learning, memory, and cognitive growth
Theory-of-Mind, episodic and autobiographical memory in cognitive systems
Introspection, metacognitive reasoning and self-awareness in BICA
Models of learning and memory: robustness, flexibility, transferability
Natural and body language and its role in intelligence, cognition and interaction
Unifying frameworks and constraints for cognitive architectures: the grand unification

Artificial Intelligence:

Creativity, goal reasoning and human-level autonomy in artifacts
Embodied vs. ambient intelligence: embedding or embodiment?
Natural Language capabilities and social competence of BICA
Learning by reading, by observation, by reasoning and by analogy
Robust and scalable machine learning mechanisms in BICA
Self-regulated learning, bootstrapped and meta-learning and the critical mass
The place for BICA in tomorrow's textbook of artificial intelligence

General:

Mathematical basis for BICA and fundamental theoretical questions in BICA research
Alternative substrates for implementation of BICA: smart materials, quantum and biocomputing
Alternative approaches to the development of BICA: evolutionary, system-theoretic, educational
Fundamental academic, practical and theoretical questions in BICA research and technology
Cognitive Decathlon and Grand Challenges for BICA as components of the BICA Challenge
Critical mass for a universal human-level learner and a roadmap to solving the BICA Challenge
Metrics, tests, proximity measures and the roadmap to human-level / human-compatible AI
Leveraging the cloud, world-wide-web, and social-media: possible role for BICA?
Cybersecurity and secure authentication methods based on BICA
Interdisciplinary research opportunities involving BICA
International trends and opportunities in funding BICA related research