Building autonomous systems that achieve human level intelligence is one of the primary objectives in artificial intelligence (AI). It requires the study of a wide range of functions robustly across different phases of human cognition. This paper presents a review of agent cognitive architectures in the past 20 year’s AI research. Different from software structures and simulation environments, most of the architectures concerned are established from mathematics and philosophy. They are categorized according to their knowledge processing patterns—symbolic, emergent or hybrid. All the relevant literature can be accessed publicly, particularly through the Internet. Available websites are also summarized for further reference.

A Survey of Cognitive Architectures in the Past 20 Years

We present a methodology for designing and implementing interactive intelligences. The constructionist design methodology (CDM) -- so called because it advocates modular building blocks and incorporation of prior work -- addresses factors that we see as key to future advances in AI, including support for interdisciplinary collaboration, coordination of teams, and large-scale systems integration. We test the methodology by building an interactive multifunctional system with a real-time perception- action loop. The system, whose construction relied entirely on the methodology, consists of an embodied virtual agent that can perceive both real and virtual objects in an augmented-reality room and interact with a user through coordinated gestures and speech. Wireless tracking technologies give the agent awareness of the environment and the user's speech and communicative acts. User and agent can communicate about things in the environment, their placement, and their function, as well as about more abstract topics, such as current news, through situated multimodal dialogue. The results demonstrate the CDM's strength in simplifying the modeling of complex, multifunctional systems that require architectural experimentation and exploration of unclear subsystem boundaries, undefined variables, and tangled data flow and control hierarchies.

/pdf/constructionist-design-methodology-for-interactive-kmlhg0xz2p.pdf

Constructionist Design Methodology for Interactive Intelligences

This paper presents a type of scheduling blackboard called whiteboards. Blackboards can simplify construction of systems with large numbers of heterogeneous components requiring a high number of fine-grained interactions. An increase in systems integration, for example in humanoid robotics and intelligent environments, has called for better solutions to support multi-module integration. Whiteboards extend the blackboard model in a number of significant ways that allow them to fill this role. Chief among their features are: an explicit temporal model; quality of service; both publish-subscribe and querying for data; both discrete and streaming data using the same API; explicit data wrappers; programming language independence; as well as a number of solutions to practical issues for improving development effort and runtime performance. Whiteboards consist of (i) a general-purpose message type format, (ii) ontologically-defined message and data stream types, and (iii) specifications for routing between system components. Whiteboards thus provide a development tool especially relevant for simulations of complex natural systems where symbolic data meets raw numerical data; systems with illdefined boundaries between sub-systems; and systems where the number of component states and interactions is considered to be relatively large. Examples include computer vision, speech recognition and robotics, ecosystems and biological systems. This paper describes the main constituents of whiteboards and their use.

/pdf/whiteboards-scheduling-blackboards-for-semantic-routing-of-t9nzqtnxv9.pdf

Whiteboards: Scheduling Blackboards for Semantic Routing of Messages & Streams

Spartacus is our robot entry in the 2005 AAAI Mobile Robot Challenge, making a robot attend the National Conference on Artificial Intelligence. Designing robots that are capable of interacting with humans in real-life settings can be considered the ultimate challenge when it comes to intelligent autonomous systems. One key issue is the integration of multiple modalities (e.g., mobility, physical structure, navigation, vision, audition, dialogue, reasoning). Such integration increases the diversity and also the complexity of interactions the robot can generate. It also makes it difficult to monitor how such increased capabilities are used in unconstrained conditions, whether it is done while the robot is in operation of afterwards. This paper reports solutions and findings resulting from our hardware, software and decisional integration work on Spartacus. It also outlines perspectives in making intelligent and interaction capabilities evolve for autonomous robots.

Spartacus attending the 2005 AAAI conference

The Twelfth Annual American Association for Artificial Intelligence (AAAI) Robot Competition and Exhibition was held in Acapulco, Mexico, in conjunction with the Eighteenth International Joint Conference on Artificial Intelligence. The events included the Robot Host and Urban Search and Rescue competitions, the AAAI Robot Challenge, and the Robot Exhibition. In the Robot Host event, the robots had to act as mobile information servers and guides to the exhibit area of the conference. In the Urban Search and Rescue competition, teams attempted to find victims in a simulated disaster area using teleoperated, semiautomonous, and autonomous robots. The AAAI Robot Challenge is a noncompetitive event where the robots attempt to attend the conference by locating the registration booth, registering for the conference, and then giving a talk to an audience. Finally, the Robot Exhibition is an opportunity for robotics researchers to demonstrate their robots' capabilities to conference attendees. The three days of events were capped by the two Robot Challenge participants giving talks and answering questions from the audience.

/pdf/2003-aaai-robot-competition-and-exhibition-2csjl9vgcy.pdf

2003 AAAI robot competition and exhibition

This article describes the winning entries in the 2000 Association for the Advancement of Artificial Intelligence Mobile Robot Competition. The robots, developed by Swarthmore College, all used a modular hybrid architecture designed to enable reflexive responses to perceptual input. Within this architecture, the robots integrated visual sensing, speech synthesis and recognition, the display of an animated face, navigation, and interrobot communication. In the Hors d'Oeuvres, Anyone? event, a team of robots entertained the crowd while they interactively served cookies; and in the Urban Search-and-Rescue event, a single robot autonomously explored a section of the test area, identified interesting features, built an annotated map, and exited the test area within the allotted time.

/pdf/reaper-a-reflexive-architecture-for-perceptive-agents-1f19t5sxuy.pdf

REAPER: A Reflexive Architecture for Perceptive Agents

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REAPER: A Reflexive Architecture for Perceptive Agents