Many researchers are working towards the goal of a semantic Web --- a Web that provides information in a way that is useful to artificial intelligences.

Behavior-Oriented Design (BOD) is a development methodology for creating complex, complete agents such as virtual-reality characters, autonomous robots, intelligent tutors. BOD agents are modular, but not multi-agent systems.

We present a methodology for designing and implementing interactive intelligences. The Constructionist Methodology – so called because it advocates modular building blocks and incorporation of prior work – addresses factors that we see as key to future advances in A.I., including interdisciplinary collaboration support, coordination of teams and large-scale systems integration. We test the methodology by building an interactive multi-functional 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 function, as well as more abstract topics such as current news, through situated multimodal dialog. The results demonstrate the Constructionist Methodology’s strength in simplifying the modeling of complex, multi-functional systems requiring architectural experimentation and exploration of unclear sub-system boundaries, undefined variables, and tangled data flow and control hierarchies.

Abstract. This paper presents the behaviour-based control architecture iB2C (integrated Behaviour-Based Control) used for the development of complex robotic systems. The specification of behavioural components is described as well as the integration of behaviour coordination and hierarchical abstraction. It is considered how the design process can be supported by guidelines and by tools for development as well as analysis. Finally some application platforms are presented and a step by step description of building up a behaviour-based control structure for an outdoor robot is given. Key words: behaviour-based control, system analysis, architecture design 1

Abstract. Engineering non-trivial open multi-agent systems is a challenging task. Our research focusses on situated multi-agent systems, i.e. systems in which agents are explicitly placed in an environment which agents can perceive and in which they can act. Situated agents do not use long-term planning to decide what action sequence should be executed, but select actions based on the locally perceived state of the world and limited internal state. To cope with change and dynamism of the system, situated agents must be able to adapt their behavior. A well-known family of agent architectures for adaptive behavior are free-flow architectures. However, building a free-flow architecture based on an analysis of the problem domain is a quasi-impossible job for non-trivial agents. To tackle the complexity of designing adaptive agent behavior based on a free-flow architecture, suitable abstractions are needed to describe and structure the agent behavior. The abstraction of a role is obviously essential in this respect. A modeling language is needed as well to model the behavior of the agents. We propose a statechart modeling language to support the design of roles for situated agents. In this paper we describe a design process for adaptive behavior of situated agents as part of a multi-agent oriented methodology. The design process integrates the abstraction of a role with a free-flow architecture. Starting from the results of analysis of the problem domain, the designer incrementally refines the model of the agent behavior. The resulting class diagram serves as a basis for implementation. We illustrate the subsequent design steps with a case study on controlling a collection of automated guided vehicles. 1

This proposal was originally a short paper relating representations of intelligence between three fields: psychology, neuroscience and artificial intelligence (AI). I particularly emphasize the role of modularity in these three areas. To my knowledge, this paper was never published — it was written on commission, but several years ago and I have just done yet another web search to find it. Further,

Abstract. This paper presents a case study for using a relatively recently developed methodology, Behavior Oriented Design, to develop an Intelligent Virtual Agent (IVA). Our usability study was conducted in Unreal Tournament using the game Capture The Flag. The final agent displays reasonably competent behavior: she is able to pursue multiple goals simultaneously and produce well-ordered behavior. 1

Learning, like any search, is only tractable for situated, resource-constrained agents if it is tightly focused. Adaptation is only worth the risks inherent in changing a complicated intelligence if it is very likely to improve the agent's performance on its goal tasks. Modularity is one tool for providing the information a learning system needs: it facilitates the use of a specialized representation suitable to a particular learning task, and provides for specialized perception to inform that representation. This paper begins by examining why behavior-based artificial intelligence, a well-known modular theory of intelligent design, has not so-far been used systematically to support such an approach. It then describes a new design methodology, behavior-oriented design (BOD), which does. Examples, drawn from both mobile robotics and models of learning in non-human primates, show the sorts of information such an approach can support, including both explicit and implicit anticipatory representations.

The `Radical Agent Concept' in this chapter is that communication between agents in a MAS should be the simplest part of the system. When extensive real-time coordination between modules is required, then those modules should probably be considered elements of a single modular agent rather than as agents themselves. The advantage of this distinction is that system developers can then leverage standard software-engineering practices and more centralized coordination mechanisms to reduce the over-all complexity of the system. In this chapter I provide arguments for this point and also examples, both from nature and from my own research in building modular agents.

Abstract. Engineering non-trivial open multi-agent systems is a challenging task. Our research focusses on situated multi-agent systems, i.e. systems in which agents are explicitly placed in a context – an environment – which agents can perceive and in which they can act. Two concerns are essential in developing such open systems. First, the agents must be adaptive in order to exhibit suitable behavior in changing circumstances of the system: new agents may join the system, others may leave, the environment may change, e.g. its topology or its characteristics such as throughput and visibility. A well-known family of agent architectures for adaptive behavior are free-flow architectures. However, building a free-flow architecture based on an analysis of the problem domain is a quasi-impossible job for non-trivial agents. Second, multi-agent systems developers as software engineers require suitable abstractions for describing and structuring agent behavior. The abstraction of a role obviously is essential in this respect. Earlier, we proposed statecharts as a formalism to describe roles. Although this allows application developers to describe roles comfortably, the formalism supports rigid behavior only, and hampers adaptive behavior in changing environments. In this paper we describe how a synergy can be reached between freeflow architectures and statechart models in order to combine the best of both worlds: adaptivity and suitable abstractions. We illustrate the result through a case study on controlling a collection of automated guided vehicles (AGVs), which is the subject of an industrial project. 1