The complexity of AI characters in computer games is continually improving; however they still fall short of human players. In this paper we describe an AI bot for the game Quake II that tries to incorporate some of those missing capabilities. This bot is distinguished by its ability to build its own map as it explores a level, use a wide variety of tactics based on its internal map, and in some cases, anticipate its opponent's actions. The bot was developed in the Soar architecture and uses dynamical hierarchical task decomposition to organize it knowledge and actions. It also uses internal prediction based on its own tactics to anticipate its opponent's actions. This paper describes the implementation, its strengths and weaknesses, and discusses future research.

ion-Partitioned Evaluator (APE) architecture which has been tested in a simulated, single-agent, indoor navigation domain [SH90]. The APE architecture is composed of a number of concurrent, hierarchically abstract action control layers, each representing and reasoning about some particular aspect of the agent's task domain. Implemented as a parallel blackboard-based planner, the five layers --- sensor/motor, spatial, temporal, causal, and conventional (general knowledge) --- effectively partition the agent's data processing duties along a number of dimensions including temporal granularity, information/resource use, and functional abstraction. Perceptual information flows strictly from the agent sensors (connected to the sensor /motor level) toward the higher levels, while command or goal-achievement information flows strictly downward towards the agent's effectors (also connected to the sensor/motor level). Besides mechanisms for communicating with other layers, each layer in the AP...

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible...

We present an autonomous agent architecture and its component subsystems that integrate important abilities needed for robust, flexible performance in dynamic environments. These abilities involve appropriate reaction to environmental situations given the agent's goals; selective attention to multiple, competing goals; planning new action routines when innovation beyond designer-provided routines is necessary; and learning the effects of actions so that the planner can use them to build ever more reliable plans. The teleo-reactive format allows actions to be closely coupled to continuous environmental feedback and is also especially compatible with conventional AI planning and learning mechanisms. The workings of the proposed architecture and its subsystems are illustrated in a simulated robot domain. We conclude by noting areas where future work is needed.

The origins of the Soar architecture can be traced back to the seminal research of Allen Newell and Herbert Simon on symbol systems, heuristic search, goals, problem spaces, and production systems. Since its official inception in 1982, Soar has evolved through six major releases, as both an AI architecture and as the basis for a unified theory of cognition. This paper traces this evolutionary path, starting with Soar's intellectual roots, and then proceeding through the stages defined by the six major system releases. Each stage is characterized with respect to a hierarchy of four levels of analysis: the knowledge level, the problem space level, the symbolic architecture level, and the implementation level.

We have constructed a team of intelligent agents that perform the tasks of an attack helicopter company for a synthetic battlefield environment used for running largescale military exercises. We have used the Soar integrated architecture to develop: (1) pilot agents for a company of helicopters, (2) a command agent that makes decisions and plans for the helicopter company, and (3) an approach to teamwork that enables the pilot agents to coordinate their activities in accomplishing the goals of the company. This case study describes the task domain and architecture of our application, as well as the benefits and lessons learned from applying AI technology to this domain.

This paper describes an organizing conceptual structure for current real-time AI research, clarifying the different meanings this term has acquired for various researchers

This paper presents Reaction-First Search (rfs), an incremental planning algorithm that produces plans for execution by a reactive system.

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial...

The original goal of artificial intelligence was the design and construction of computational artifacts that combined many cognitive abilities in an integrated system. These entities were intended to have the same intellectual capacity as humans and they were supposed to exhibit their intelligence in a general way across many different domains. We will refer to this research agenda as aimed at the creation of general intelligent systems. Unfortunately, modern artificial intelligence has largely abandoned this objective, having instead divided into many distinct subfields that care little about generality, intelligence, or even systems. Subfields like computational linguistics, planning, and computer vision focus their attention on specific components that underlie intelligent behavior, but seldom show concern about how they might interact with each other. Subfields like knowledge representation and machine learning focus on idealized tasks like inheritance, classification, and reactive control that ignore the richness and complexity of human intelligence. The fragmentation of artificial intelligence has taken energy away from efforts on general intelligent systems, but it has led to certain types of progress within each of its subfields. Despite this subdivision into distinct communities, the past decade has seen many applications of AI technology