Stuart C. Shapiro

AAAI Conference 2005 System Paper

MGLAIR Agents in Virtual and Other Graphical Environments

• Stuart C. Shapiro
• David E. Pape
• Michael Kandefer

We are demonstrating several intelligent agents built according to the MGLAIR (Modal Grounded Layered Architecture with Integrated Reasoning) agent architecture. The top layer of MGLAIR is implemented in SNePS and its acting subsystem, SNeRE (the SNePS Rational Engine). The major demonstration will be act 3 of The Trial The Trail, an interactive drama running on an immersive Virtual Reality system, in which a human participant interacts with several MGLAIR actor-agents. We will also demonstrate several other MGLAIR agents that operate in non-VR graphical environments. All these agents illustrate our approach to building agents with integrated first-person, on-line reasoning and acting.

AAAI Conference 2004 Short Paper

Identifying an Object that is Perceptually Indistinguishable from One Previously Perceived

• John F. Santore
• Stuart C. Shapiro

People often encounter objects that are perceptually indistinguishable from objects that they have seen before. When this happens, how do they decide whether the object they are looking at is something never before seen, or if it is the same one they encountered before? To identify these objects people surely use background knowledge and contextual cues. We propose a computational theory of identifying perceptually indistinguishable objects (PIOs) based on a set of experiments which were designed to identify the knowledge and perceptual cues that people use to identify PIOs. By identifying a PIO, we mean deciding which individual object is encountered, not deciding what category of objects it belongs to. In particular, identifying a PIO means deciding if the object just encountered is a new, never before seen object, or if it has been previously encountered, which previously perceived object it is. Our agent’s beliefs and reasoning are based on an intensional representation. Intensional representations model the sense of an object rather than the object referent, itself. The terms of our representation language, SNePS, denote mental entities. Some such entities are propositions; others are abstract ideas; others are the agent’s “concepts” or “ideas” of objects in the world. This is important for the task of identifying PIOs, because before the identification task is complete, the agent may have two mental entities, e1 and e2, that it might or might not conclude correspond to the same object in the world.

AAAI Conference 2004 Short Paper

Knowledge State Reconsideration: Hindsight Belief Revision

• Frances L. Johnson
• Stuart C. Shapiro

As a knowledge representation and reasoning (KRR) system gathers and reasons about information, it has to update its belief space to maintain consistency. Some belief change operations it can perform include expansion (addition with no consistency checking), contraction (aka removal or retraction), revision (consistent prioritized addition), and consolidation (elimination of any and all inconsistencies). Whether belief change operations are performed on theories or bases, with ideal agents or those that are resource-bounded, there is no doubt that the order of operations typically affects the makeup of the resulting belief base. If a KRR system gains new information that, in hindsight, might have altered the outcome of an earlier belief change decision, the earlier decision should be re-examined. We call this operation reconsideration, and the result is an optimal belief base regardless of the order of previous belief change operations.

AIJ Journal 1988 Journal Article

A model for belief revision

• João P. Martins
• Stuart C. Shapiro

It is generally recognized that the possibility of detecting contradictions and identifying their sources is an important feature of an intelligent system. Systems that are able to detect contradictions, identify their causes, or readjust their knowledge bases to remove the contradiction, called Belief Revision Systems, Truth Maintenance Systems, or Reason Maintenance Systems, have been studied by several researchers in Artificial Intelligence (AI). In this paper, we present a logic suitable for supporting belief revision systems, discuss the properties that a belief revision system based on this logic will exhibit, and present a particular implementation of our model of a belief revision system. The system we present differs from most of the systems developed so far in three respects: First, it is based on a logic that was developed to support belief revision systems. Second, it uses the rules of inference of the logic to automatically compute the dependencies among propositions rather than having to force the user to do this, as in many existing systems. Third, it was the first belief revision system whose implementation relies on the manipulation of sets of assumptions, not justifications.

AAAI Conference 1988 Conference Paper

Automatic Construction of User-lnterface Displays

• Yigal Arens
• Stuart C. Shapiro

Construction of user interfaces for most computer applications remains time consuming and difficult. This is particularly true when the user interface system must dynamically create displays integrating the use of several interface modes. This paper shows how Artificial Intelligence knowledge base and rule technology can be used to address this problem. NIKL is used to model the entities of the application domain and the facilities of the user interface. Rules are written connecting the two models. These rules range from application specific to general rules of presentation. The situation to be displayed is asserted into a PENN1 database. A Presentation Designer interprets this data using the domain model, chooses the appropriate rules to use in creating the display, and creates a description of the desired display in terms of the interface model. A system, Integrated Interfaces, using this design for an integrated multi-modal map graphics, natural language, menu, and form interface has been created and applied to a database reporting application.

IJCAI Conference 1987 Conference Paper

A Representation for Natural Category Systems

• Sandra L. Peters
• Stuart C. Shapiro

IJCAI Conference 1987 Conference Paper

Graphical Deep Knowledge for Intelligent Machine Drafting

• James Geller
• Stuart C. Shapiro

AAAI Conference 1986 Conference Paper

SNePS Considered as a Fully Intensional Propositional Semantic Network

• Stuart C. Shapiro

We present a formal syntax and semantics for SNePS considered as the (modeled) mind of a cognitive agent. The semantics is based on a Meinongian theory of the intensional objects of thought that is appropriate for AI considered as "computational philosophy" or "computational psychology".

TARK Conference 1986 Conference Paper

Theoretical Foundations for Belief Revision

• João P. Martins
• Stuart C. Shapiro

Belief revision systems are AI programs that deal with contradictions. They work with a knowledge base, performing reasoning from the propositions in the knowledge base, "filtering" those propositions so that only part of the knowledge base is perceived - the set of propositions that are under consideration. This set of propositions is called the set of believed propositions. Typically, belief revision systems explore alternatives, make choices, explore the consequences of their choices, and compare results obtained when using different choices. If during this process a contradiction is detected, then the belief revision system will revise the knowledge base, "erasing" some propositions so that it gets rid of the contradiction. In this paper, we present a logic suitable to support belief revision systems and discuss the properties that a belief revision system based on this logic will exhibit. The system we present, SWM, differs from most of the systems developed so far in two respects: First, it is based on a logic which was developed to support belief revision systems. Second, its implementation relies on the manipulation of sets of assumptions, not justifications. The first feature allows the study of the formal properties of the system independently of its implementation, and the second one enables the system to work effectively and efficiently with inconsistent information, to switch reasoning contexts without processing overhead, and to avoid most backtracking.

NMR Workshop 1984 Conference Paper

A Model for Belief Revision

• João P. Martins
• Stuart C. Shapiro

IJCAI Conference 1983 Conference Paper

Reasoning in Multiple Belief Spaces

• Joao P. Martins
• Stuart C. Shapiro

AAAI Conference 1980 Conference Paper

Inference with Recursive Rules

• Stuart C. Shapiro

Recursiverules, such as "Yourparents'ancestors are your ancestors", althoughvery usefulfor theoremproving, naturallanguageunderstanding, questions-answe ring and information retrieval systems, presentproblemsfor many such systems, eithercausinginfiniteloopsor requiringthat arbitrarily many copiesof them be made. We have writtenan inference systemthat can use recursive ruleswithouteitherof theseproblems. The solution appearedautomatically from a technique designedto avoidredundant work. A recursive rule causesa cycleto be built in an AND/ORgraph of activeprocesses. Each pass of data throughthe cycleresultingin anotheranswer. Cyclingstops as soon as eitherthe desiredansweris produced, no more answerscan be produced, or resource boundsare exceeded.

AIJ Journal 1976 Journal Article

Artificial intelligence

• Stuart C. Shapiro

IJCAI Conference 1969 Conference Paper

A Net Structure Based Relational Question Answerer: Description and Examples,

• Stuart C. Shapiro
• George H. Woodmansee

A question answering system is described which uses a net structure for storage of infor­ mation. The net structure consists of nodes and labelled edges, which represent relations be­ tween the nodes. The labels are also nodes, and therefore definitions of relations may be stored in the net. It is demonstrated that the generality and complexity of this memory struc­ ture allows a surprisingly powerful question an­ swering system to be constructed using comparitively simple executive routines. Output from the question answerer, which is currently run­ ning on an interactive, time sharing system, is included, showing its range of applicability i n ­ cluding question answering, inductive and de­ ductive inference, simple theorem proving and problem solving.