Episodic memory endows agents with numerous general cognitive capabilities, such as action modeling and virtual sensing. However, for long-lived agents, there are numerous unexplored computational challenges in supporting useful episodic-memory functions while maintaining real-time reactivity. In this paper, we review the implementation of episodic memory in Soar and present an expansive evaluation of that system. We demonstrate useful applications of episodic memory across a variety of domains, including games, mobile robotics, planning, and linguistics. In these domains, we characterize properties of environments, tasks, and episodic cues that affect performance, and evaluate the ability of Soar’s episodic memory to support hours to days of real-time operation.

In most cognitive architectures, episodic memory is either not implemented, or plays a secondary role. In contrast, in the Xapagy architecture episodic memory is the primary means of acquiring and using knowledge. Shadowing, the main reasoning method of the system, relies on unprocessed historical recordings of concrete events to determine the agent's behavior. This paper outlines the use of episodic memory in Xapagy, and investigates whether episodic memory might play a wider role in cognitive architectures at large.

Evidence indicates that episodic memory plays an important role in general cognition. A modest body of research exists for creating artificial episodic memory systems. To date, research has focused on exploring their benefits. As a result, existing episodic memory systems rely on a small, relevant memory cue for effective memory retrieval. We present Ziggurat, a domain-independent episodic memory structure and accompanying episodic learning algorithm that learns the temporal context of recorded episodes. Ziggurat's context-based memory retrieval means that it does not have to rely on relevant agent cues for effective memory retrieval; it also allows an agent to dynamically make plans using past experiences. In our experimental trials in two different domains, Ziggurat performs as well or better than an episodic memory implementation based on most other systems.

Defeasible inheritance networks are a non-monotonic framework that deals with hierarchical knowledge. On the other hand, rational closure is acknowledged as a landmark of the preferential approach. We will combine these two approaches and define a new non-monotonic closure operation for propositional knowledge bases that combines the advantages of both. Then we redefine such a procedure for Description Logics, a family of logics well-suited to model structured information. In both cases we will provide a simple reasoning method that is build on top of the classical entailment relation.

The phenomenon of episodic memory has been studied for over thirty years, but it is only recently that its constructive nature has been shown to be closely linked to the processes underpinning imagination. This paper builds on recent work by the authors in developing architectures for a form of imagination suitable for use in artifacts, and considers how these architectures might be extended to provide a form of episodic memory.

In the restaurant script, notated as $RESTAURANT, the roles might directly to the United Press International Yale researchers explored intentionality include customer, waitress, and cook; news wire and could skim news One of the earliest programs to the props could be a menu, table, and stories in dozens of different domains, embody goals and plans within the silverware; the locations could be the and produce summaries in several languages. CD paradigm was Jim Meehan's bar, dining area, and kitchen; and the On the DEC-20 (which by TALESPIN, which made up stories events would include arriving, seating, 1978 had replaced the PDP-101, similar to the fables of Aesop.

Over the past few years, the notion of a "prototype" (e.g., TYPICAL-ELEPHANT) seems to have caught on securely in knowledge representation research. Along with a way to specify default properties for instances of a description, proto-representations allow overriding, or "canceling" of properties that don't apply in particular cases. This supposedly makes representing exceptions ( three-legged elephants and the like ) easy; but, alas, it makes one crucial type of representation impossible-that of composite descriptions whose meanings are functions of the structure and interrelation of their parts. This article explores this and other ramifications of the emphasis on default properties and "typical" objects.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented.

This is an excerpt from the Handbook of Artificial Intelligence, a compendium of hundreds of articles about AI ideas, techniques, and programs being prepared at Stanford University by AI researchers and students from across the country. In addition to articles describing the specifics of various AI programming methods, the Handbook contains dozens of overview articles like this one, which attempt to give historical and scientific perspective to work in the different areas of AI research. This article is from the Handbook chapter on natural language understanding. Cross-references to other articles in the handbook have been removed-terms discussed in more detail elsewhere are italicized. Many people have contributed to this chapter, including especially Anne Gardner, James Davidson, and Terry Winograd. Avron Barr and Edward A. Feigenbaum are the Handbook's general editors.

... we began to program a computer understanding system thatwould attempt to process input texts. An item crucial to our ability to accomplishthis task was what we called a script. A script is a frequently repeated causalchain of events that describes a standard situation. In understanding, when it ispossible to notice that one of these standard event chains has been initiated,then it is possible to understand predictively. That is, if we know we are in arestaurant then we can understand where an "order" fits with what we justheard, who might be ordering what from whom, what preconditions (menu,sitting down) might have preceded the "order", and what is likely to happennext. All this information comes from the restaurant script.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.