The goal of our research effort is to develop generic technology for intelligent automated agents in simulation environments. These agents are to behave believably like humans in these environments. In this context, believability refers to the indistinguishability of these agents from humans, given the task being performed, its scope, and the allowable mode(s) of interaction during task performance. For instance, for a given simulation task, one allowable mode of interaction with an agent may be typewritten questions and answers on a limited subject matter. Alternatively, a different allowable mode of interaction for the same (or different) task may be speech rather than typewritten words. In all these cases, believability implies that the agent must be indistinguishable from a human, given the particular mode of interaction.
In a dynamic, multi-agent environment, an automated intelligent agent is often faced with the possibility that other agents may instigate events that actually hinder or help the achievement of its own goals. To act intelligently in such an environment, an automated agent needs an event tracking capability to continually monitor the occurrence of such events and the temporal relationships among them. This capability enables an agent to infer the occurrence of important unobserved events as well as obtain a better understanding of interaction among events. This paper focuses on event tracking in one complex and dynamic multi-agent environment: the air-combat simulation environment. It analyzes the challenges that an automated pilot agent must face when tracking events in this environment. This analysis reveals some novel constraints on event tracking that arise from complex multi-agent interactions. The paper proposes one solution to address these constraints, and demonstrates it using a simple re-implementation of an existing automated pilot agent.
This paper focuses on event tracking in one complex and dynamic multi-agent environment: the air-combat simulation environment. It analyzes the challenges that an automated pilot agent must face when tracking events in this environment. This analysis reveals three new issues that have not been addressed in previous work in this area: (i) tracking events generated by agents' flexible and reactive behaviors, (ii) tracking events in the context of continuous agent interactions, and (iii) tracking events in real-time. The paper proposes one solution to address these issues. One key idea in this solution is that the (architectural) mechanisms that an agent employs in generating its own flexible and reactive behaviors can be used to track other agents' flexible and reactive behaviors in real-time. A second key idea is the use of a world-centered representation for modeling agent interactions. The solution is demonstrated using an implementation of an automated pilot agent.
time, flexibly use a small amount of tactical This article reports on recent progress in the development of TacAir-Soar, an intelligent automated agent for tactical air simulation. This includes progress in expanding the agent’s coverage of the tactical air domain, progress in enhancing the quality of the agent’s behavior, and progress in building an infrastructure for research and development in this area. knowledge about two classes of one-versusone (1-v-1) Beyond Visual Range (BVR) tactical air scenarios. In the non-jinking bogey scenarios, one plane (the non-jinking bogey) is unarmed and maintains a straight-and-level flight path. The other plane is armed with long-range radar-guided, medium-range radar-guided, and short-range infrared-guided missiles. Its task is to set up for a sequence of missile shots, at
Eliminating combinatorics from the match in production systems (or rule-based systems) is important for expert systems, real-time performance, machine learning (particularly with respect to the utility issue), parallel implementations and cognitive modeling. In , the unique-attribute representation was introduced to eliminate combinatorics from the match. However, in so doing, unique-attributes engender a sufficiently negative set of trade-offs, so that investigating whether there are alternative representations that yield better trade-offs becomes of critical importance.
This article identifies two promising spaces of such alternatives, and explores a number of the alternatives within these spaces. The first space is generated from local syntactic restrictions on working memory. Within this space, unique-attributes is shown to be the best alternative possible. The second space comes from restrictions on the search performed during the match of individual productions (match-search). In particular, this space is derived from the combination of a new, more relaxed, match formulation (instantiationless match) and a set of restrictions derived from the constraint-satisfaction literature. Within this space, new alternatives are found that outperform unique-attributes in some, but not yet all, domains.