Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

The SAL integrated cognitive architecture

Over the last two decades, the complementary properties of symbolic and connectionist systems have led to a number of attempts at hybridizing the two approaches to leverage their strengths and alleviate their shortcomings. The fact that those attempts have generally fallen short of their goals largely reflects the difficulties in integrating computational paradigms of a very different nature without sacrificing their key properties in the process. In this paper, we propose that biological plausibility can serve as a powerful constraint to guide the integration of hybrid intelligent systems. We introduce a hybrid cognitive architecture called SAL, for “Synthesis of ACT-R and Leabra”. ACT-R and Leabra are cognitive architectures in the symbolic and connectionist tradition, respectively. Despite widely different origins and levels of abstraction, they have evolved considerable commonalities in response to a joint set of constraints including behavioral, physiological, and brain imaging data. We introduce the ACT-R and Leabra cognitive architectures and their similarities in structures and concepts then describe one possible instantiation of the SAL architecture based on a modular composition of its constituent architectures. We illustrate the benefits of the integration by describing an application of the architecture to autonomous navigation in a virtual environment and discuss future research directions

The SAL integrated cognitive architecture

Over the last two decades, the complementary properties of symbolic and connectionist systems have led to a number of attempts at hybridizing the two approaches to leverage their strengths and alleviate their shortcomings. The fact that those attempts have generally fallen short of their goals largely reflects the difficulties in integrating computational paradigms of a very different nature without sacrificing their key properties in the process. In this paper, we propose that biological plausibility can serve as a powerful constraint to guide the integration of hybrid intelligent systems. We introduce a hybrid cognitive architecture called SAL, for "Synthesis of ACT-R and Leabra". ACT-R and Leabra are cognitive architectures in the symbolic and connectionist tradition, respectively. Despite widely different origins and levels of abstraction, they have evolved considerable commonalities in response to a joint set of constraints including behavioral, physiological, and brain imaging data. We introduce the ACT-R and Leabra cognitive architectures and their similarities in structures and concepts then describe one possible instantiation of the SAL architecture based on a modular composition of its constituent architectures. We illustrate the benefits of the integration by describing an application of the architecture to autonomous navigation in a virtual environment and discuss future research directions

The SAL integrated cognitive architecture

Over the last two decades, the complementary properties of symbolic and connectionist systems have led to a number of attempts at hybridizing the two approaches to leverage their strengths and alleviate their shortcomings. The fact that those attempts have generally fallen short of their goals largely reflects the difficulties in integrating computational paradigms of a very different nature without sacrificing their key properties in the process. In this paper, we propose that biological plausibility can serve as a powerful constraint to guide the integration of hybrid intelligent systems. We introduce a hybrid cognitive architecture called SAL, for “Synthesis of ACT-R and Leabra”. ACT-R and Leabra are cognitive architectures in the symbolic and connectionist tradition, respectively. Despite widely different origins and levels of abstraction, they have evolved considerable commonalities in response to a joint set of constraints including behavioral, physiological, and brain imaging data. We introduce the ACT-R and Leabra cognitive architectures and their similarities in structures and concepts then describe one possible instantiation of the SAL architecture based on a modular composition of its constituent architectures. We illustrate the benefits of the integration by describing an application of the architecture to autonomous navigation in a virtual environment and discuss future research directions