Jay Earley

Bio: Jay Earley is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Parsing & Data structure. The author has an hindex of 10, co-authored 11 publications receiving 2961 citations.

An efficient context-free parsing algorithm

Abstract: A parsing algorithm which seems to be the most efficient general context-free algorithm known is described. It is similar to both Knuth's LR(k) algorithm and the familiar top-down algorithm. It has a time bound proportional to n3 (where n is the length of the string being parsed) in general; it has an n2 bound for unambiguous grammars; and it runs in linear time on a large class of grammars, which seems to include most practical context-free programming language grammars. In an empirical comparison it appears to be superior to the top-down and bottom-up algorithms studied by Griffiths and Petrick.

An efficient context-free parsing algorithm

Abstract: A parsing algorithm which seems to be the most efficient general context-free algorithm known is described. It is similar to both Knuth's LR(k) algorithm and the familiar top-down algorithm. It has a time bound proportional to n3 (where n is the length of the string being parsed) in general; it has an n2 bound for unambiguous grammars; and it runs in linear time on a large class of grammars, which seems to include most practical context-free programming language grammars. In an empirical comparison it appears to be superior to the top-down and bottom-up algorithms studied by Griffiths and Petrick.

Toward an understanding of data structures

Abstract: This paper presents a notation and formalism for describing the semantics of data structures. This is based on directed graphs with named edges and transformations on these graphs. In addition, and implementation facility is described which could be part of a programming language, which allows a programmer who has expressed the semantics of an algorithm in terms of the graphs to then specify the implementation of some of his data structures in order to gain efficiency.

A formalism for translator interactions

Abstract: A formalism is presented for describing the actions of processors for programming languages—compilers, interpreters, assemblers—and their interactions in complex systems such as compiler-compilers or extendible languages. The formalism here might be used to define and answer such a question as “Can one do bootstrapping using a metacompiler whose metaphase is interpretive?” In addition an algorithm is presented for deciding whether or not a given system can be produced from a given set of component processors.

Speech and Language Processing

Abstract: is one of the most recognizablecharacters in 20th century cinema. HAL is an artificial agent capable of such advancedlanguage behavior as speaking and understanding English, and at a crucial moment inthe plot, even reading lips. It is now clear that HAL’s creator, Arthur C. Clarke, wasa little optimistic in predicting when an artificial agent such as HAL would be avail-able. But just how far off was he? What would it take to create at least the language-relatedpartsofHAL?WecallprogramslikeHALthatconversewithhumansinnatural

Machine Recognition of Human Activities: A Survey

Abstract: The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.

Transition network grammars for natural language analysis

Abstract: The use of augmented transition network grammars for the analysis of natural language sentences is described Structure-building actions associated with the arcs of the grammar network allow for the reordering, restructuring, and copying of constituents necessary to produce deep-structure representations of the type normally obtained from a transformational analysis, and conditions on the arcs allow for a powerful selectivity which can rule out meaningless analyses and take advantage of semantic information to guide the parsing The advantages of this model for natural language analysis are discussed in detail and illustrated by examples An implementation of an experimental parsing system for transition network grammars is briefly described

A universal modular ACTOR formalism for artificial intelligence

Abstract: This paper proposes a modular ACTOR architecture and definitional method for artificial intelligence that is conceptually based on a single kind of object: actors [or, if you will, virtual processors, activation frames, or streams]. The formalism makes no presuppositions about the representation of primitive data structures and control structures. Such structures can be programmed, micro-coded, or hard wired in a uniform modular fashion. In fact it is impossible to determine whether a given object is "really" represented as a list, a vector, a hash table, a function, or a process. The architecture will efficiently run the coming generation of PLANNER-like artificial intelligence languages including those requiring a high degree of parallelism. The efficiency is gained without loss of programming generality because it only makes certain actors more efficient; it does not change their behavioral characteristics. The architecture is general with respect to control structure and does not have or need goto, interrupt, or semaphore primitives. The formalism achieves the goals that the disallowed constructs are intended to achieve by other more structured methods. PLANNER Progress "Programs should not only work, but they should appear to work as well." PDP-1X Dogma The PLANNER project is continuing research in natural and effective means for embedding knowledge in procedures. In the course of this work we have succeeded in unifying the formalism around one_ fundamental concept: the ACTOR. Intuitively, an ACTOR is an active agent which plays a role on cue according to a script. We use the ACTOR metaphor to emphasize the inseparability of control and data flow in our model. Data structures, functions, semaphores, monitors, ports, descriptions, Quillian nets, logical formulae, numbers, identifiers, demons, processes, contexts, and data bases can all be shown to be special cases of actors. All of the above are objects with certain useful modes of behavior. Our formalism shows how all of the modes of behavior can be defined in terms of one kind of behavior: sending messages to actors. An actor is always invoked uniformly in exactly the same way regardless of whether it behaves as a recursive function, data structure, or process. "It is vain to multiply Entities beyond need." William of Occam "Monotheism is the Answer." The unification and simplification of the formalisms for the procedural embedding of knowledge has a great many benefits for us: FOUNDATIONS: The concept puts procedural semantics [the theory of how things operate] on a firmer basis. It will now be possible to do cleaner theoretical studies of the relation between procedural semantics and set-theoretic semantics such as model theories of the quantificational calculus and the lambda calculus. LOGICAL CALCULAE: A procedural semantics is developed for the quantificational calculus. The logical constants FOR-ALL, THERE-EXISTS, AND, OR, NOT, and IMPLIES are defined as actors. KNOWLEDGE BASED PROGRAMMING is programming in an environment which has a substantial knowledge base in the application area for which the programs are intended. The actor formalism aids knowledge based programming in the following ways: PROCEDURAL EMBEDDING of KNOWLEDGE, TRACING BEHAVIORAL DEPENDENCIES, and SUBSTANTIATING that ACTORS SATISFY their INTENTIONS. INTENTIONS: Furthermore the confirmation of properties of procedures is made easier and more uniform. Every actor has an INTENTION which checks that the prerequisites and the context of the actor being sent the message are satisfied. The intention is the CONTRACT that the actor has with the outside world. How an actor fullfills its contract is its own business. By a SIMPLE BUG we mean an actor which does not satisfy its intention. We would like to eliminate simple debugging of actors by the META-EVALUATION of actors to show that they satisfy their intentions. Suppose that there is an external audience of actors E which satisfy the intentions.of the actors to which they send messages. Intuitively, the principle of ACTOR INDUCTION states that the intentions of all actions caused by E are in turn satisfied provided that the following condition holds: If for each actor A the' intention of A is satisfied => that the intentions of all actors sent messages by A are satisfied. Computational induction [Manna], structural induction [Burstall], and Peano induction are all special cases of ACTOR induction. Actor based intentions have the following advantages: The intention is decoupled from the actors it describes. Intentions of concurrent actions are more easily disentangled. We can more elegantly write intentions The intentions are written in the same formalism as the Because for dialogues between actors. procedures they describe. Thus for example intentions can have intentions, protection is an intrinsic property of actors, we hope to be able to deal with protection issues in the same straight forward manner as more conventional intentions. Intentions of data structures are handled by the same machinery as for all other actors. COMPARATIVE SCHEMATOLOGY: The theory of comparative power of control structures is I