Donald Nute

Defeasible Reasoning: A Philosophical Analysis in Prolog

We reason defeasibly when we reach conclusions that we might be forced to retract when faced with additional information. I contrast this with both invalid deductive reasoning and inductive reasoning. This reasoning is defeasible, but its defeasibility is not because of incorrectness. Nor is it ampliative as is inductive reasoning. It is the kind of “other things being equal” reasoning that proceeds from the assumption that we are dealing with the usual or normal case. Conclusions based on this kind of reasoning may be defeated if we find that the situation is not usual or normal.

NED-2: an agent-based decision support system for forest ecosystem management

Abstract Decision making for forest ecosystem management can include the use of a wide variety of modeling tools. These tools include vegetation growth models, wildlife models, silvicultural models, GIS, and visualization tools. NED-2 is a robust, intelligent, goal-driven decision support system that integrates tools in each of these categories. NED-2 uses a blackboard architecture and a set of semi-autonomous agents to manage these tools for the user. The blackboard integrates a Microsoft Access database and Prolog clauses, and the agents are implemented in Prolog. A graphical user interface written in Visual C++ provides powerful inventory analysis tools, dialogs for selecting timber, water, ecological, wildlife, and visual goals, and dialogs for defining treatments and building prescriptive management plans. Users can simulate management plans and perform goal analysis on different views of the management unit, where a view is determined by a management plan and a point in time. Prolog agents use growth and yield models to simulate management plans, perform goal analyses on user-specified views of the management unit, display results of plan simulation using GIS tools, and generate hypertext documents containing the results of such analysis. Individual agents use metaknowledge to set up and run external simulation models, to load rule-based models and perform inference, to set up and execute external GIS and visualization systems, and to generate hypertext reports as needed, relieving the user from performing all these tasks.

NED-1: integrated analyses for forest stewardship decisions

NED is a collective term for a set of software intended to help resource managers develop goals, assess current and potential conditions, and produce sustainable management plans for forest properties. The software tools are being developed by the USDA Forest Service, Northeastern and Southern Research Stations, in cooperation with many other collaborators. NED-1 is a Windows-based program that helps analyze forest inventory data from the perspective of various resources on management areas as large as several thousand hectares. Resources addressed include visual quality, ecology, forest health, timber, water, and wildlife. NED-1 evaluates the degree to which an individual stand or an entire management unit may provide the conditions required to accomplish specific goals. NED-1 users select from a variety of reports, including tabular data summaries, general narratives, and goal-specific analyses. An extensive hypertext system provides information about the resource goals, the desired conditions that support achieving those goals, and related data used to analyze the actual condition of the forest, as well as detailed information about the program itself and the rules and formulas used to produce the analyses. The software is constructed in C++ using an application framework; the inferencing component that handles the rule bases uses Prolog.

Goals and Goal Orientation in Decision Support Systems for Ecosystem Management

We explore a goal-oriented as opposed to a problem-oriented approach to DSS development for ecosystem management. Ecosystem management ordinarily is guided by a set of goals that may conflict in various ways. Problems are perceived obstacles to realizing goals. Identifying and resolving conflicts between goals, testing current or projected situations for goal satisfaction, and problem identification all require a robust model of the goal structure for the intended domain. The lowest level of this goal structure must be represented as desirable future conditions consisting of proposed values for observable indicators. A model of the causal, legal, and other institutional relations between these desirable future conditions is also needed. Two projects based on a goal-oriented approach to DSS development are described. The first project has produced an initial prototype that incorporates goals for forest management in rules representing three tiers: management unit goals, stand-level goals, and desirable future conditions. The second, at an initial knowledge acquisition stage, is an attempt to develop a participatory decision-making methodology for socially and environmentally sensitive economic development in Central America.

A modular translation from defeasible nets to defeasible logics

Abstract The sceptical inheritance nets introduced in Horty et al. [Proceedings of AAAI-87 (1987):358-363] are translated into a version of Nutes defeasible logic. Moreover this translation is modular in the sense of Thomason and Horty [Non-Monotonic Reasoning. Springer-Verlag (1989):234]. Apart from the importance of relating two nonmonotonic reasoning formalisms, this result shows that the reasoning mechanisms underlying defeasible logic and defeasible nets are the same. Yet they were invented independently and set in totally different contexts. This is perhaps some evidence that the underlying nonmonotonic reasoning mechanism is mainly correct. We also observe that since defeasible logics can contain both absolute and defeasible rules, they provided a uniform setting for considering nets which contain both strict and defeasible arcs.

Controlling expert system recommendations with defeasible logic

Abstract Nonmonotonic logics are alternatives to probabilistic systems for reasoning with uncertain or incomplete information. For the most part, these logics have not been implemented as automated reasoning systems or incorporated into expert systems that deal with real problems. Defeasible logic is a nonmonotonic formalism implemented in an extension of the Prolog logic programming language called d-Prolog. FORE is an initial prototype expert system for selecting a business forecasting method. Written in d-Prolog, FORE demonstrates the feasibility of using defeasible logic to control the recommendations of expert systems with a significant degree of complexity. It also serves the secondary purpose of providing a foundation for development of a mature forecasting method selection system.

Well-founded semantics for defeasible logic

Fixpoint semantics are provided for ambiguity blocking and propagating variants of Nute’s defeasible logic. The semantics are based upon the well-founded semantics for logic programs. It is shown that the logics are sound with respect to their counterpart semantics and complete for locally finite theories. Unlike some other nonmonotonic reasoning formalisms such as Reiter’s default logic, the two defeasible logics are directly skeptical and so reject floating conclusions. For defeasible theories with transitive priorities on defeasible rules, the logics are shown to satisfy versions of Cut and Cautious Monotony. For theories with either conflict sets closed under strict rules or strict rules closed under transposition, a form of Consistency Preservation is shown to hold. The differences between the two logics and other variants of defeasible logic—specifically those presented by Billington, Antoniou, Governatori, and Maher—are discussed.

Appbuilder for DSSTools : an application development environment for developing decision support systems in Prolog

Abstract A programming environment for developing complex decision support systems (DSSs) should support rapid prototyping and modular design, feature a flexible knowledge representation scheme and sound inference mechanisms, provide project management, and be domain-independent. We have previously developed DSSTools (Decision Support System Tools), a reusable, domain-independent, and open-ended toolkit for developing DSSs in Prolog. DSSTools provides modular design, a flexible knowledge representation scheme, and sound inference mechanisms to support development of any knowledge based system components of a DSS. It also provides tools for building the DSS interface and for integrating other non-Prolog components of a DSS such as simulation models, databases, or geographical information system, into a multi-component DSS. DSSTools does not provide project management, and its complex syntax makes rapid prototyping difficult. AppBuilder for DSSTools is a GUI-based application development environment for developing DSSs in DSSTools that supports rapid prototyping and project management. AppBuilder’s easy-to-use dialogues for managing and building knowledge based and top-level control components of a DSS free developers from having to memorize complex syntax and reduce development time without sacrificing the flexibility of the underlying toolkit. AppBuilder has been used to develop the Regeneration DSS, a system for predicting the regeneration of southern Appalachian hardwoods. AppBuilder is an application development environment for both prototyping and developing a complete DSS.

Classical vs Non-Classical Logics

Most philosophers writing recently on the logic of conditionals have accepted all the theses of our weak conditional logic W. No one, though, has seriously suggested that W exhausts the logic of conditionals. Indeed, almost everyone writing in the area has suggested some proper extension of W as the correct logic for conditions. The significance of W is that it appears to be one of the strongest conditional logics we can put together without generating some disagreement among the majority of these philosophers who have expressed an opinion on the subject. Of course, even W is too strong for necessitation conditionals. For the conditionals we are now considering, though, we will take W to be a lower bound on the proper logic for such conditionals.

Defeasible logic graphs: I. Theory

Abstract We propose development of an argument-based decision support system utilizing defeasible or nonmonotonic reasoning. Defeasible logic graphs (d-graphs) represent the knowledge contained in a defeasible theory. A method for propagating labels through a d-graph is developed as a means for reasoning about the theory from which the d-graph is generated. This method is proven to be sound with respect to Nutes defeasible logic and complete for finite, consistent theories with acyclic d-graphs.