A Probabilistic Reasoning Environment

Evidence-based reasoning is at the core of many problemsolving and decision-making tasks in a wide variety of domains. Generalizing from the research and development of cognitive agents in several such domains, this paper presents progress toward a computational theory for the development of instructable cognitive agents for evidence-based reasoning tasks. The paper also illustrates the application of this theory to the development of four prototype cognitive agents in domains that are critical to the government and the public sector. Two agents function as cognitive assistants, one in intelligence analysis, and the other in science education. The other two agents operate autonomously, one in cybersecurity and the other in intelligence, surveillance, and reconnaissance. The paper concludes with the directions of future research on the proposed computational theory. Developed in the framework of the scientific method, the computational approach to evidence-based reasoning views this process as ceaseless discovery of evidence, hypotheses, and arguments in a non-stationary world, involving collaborative computational processes of evidence in search of hypotheses, hypotheses in search of evidence, and evidentiary testing of hypotheses (see Figure ). First, through abductive (imaginative) reasoning that shows that something is possibly true, one generates alternative hypotheses that may explain an observation of interest or answer an important question. Next, through deductive reasoning that shows that something is ______________________________________________________________________

IEEE Transactions on Systems, Man, and Cybernetics, 1989

attention is being devoted to formalisnis for representing and processing uncertainty in automated reasoning systems. These formal algorithms rely on obtaining judgments from experts about degrees of uncertainty, or the strength of evidential relationships. A reasoning system and associated assessment methodology built upon a natural schema for an evidential argument, are discussed. This argument schema is based on the underlying causal chains linking conclusions and evidence. The framework couples a probabilistic calculus with qualitative approaches to evidential reasoning. The resulting knowledge structure leads to a natural arsessment methodology in which the expert first specifies a quulifufice argument from evidence to conclusion. Next the expert specifies a series of premkes on which the argument is based. Invalidating any of these premises would disrupt the causal link between evidence and conclusion. The final step is the assessment of the sfrengfh of the argument, in the form of degrees of belief for the premises underlying the argument. The expert may also explicitly adopt assumptions affecting the strength of evidential arguments. A higher-level "metareasoning" process is described, in which assumptions underlying the strength and direction of evidential arguments may be revised in response to conflict.

2012

In this paper, we extend Dung's seminal argument framework to form a probabilistic argument framework by associating probabilities with arguments and defeats. We then compute the likelihood of some set of arguments appearing within an arbitrary argument framework induced from this probabilistic framework.

International Joint Conference on Artificial Intelligence, 1999

We describe an interactive system which supports the exploration of arguments generated from Bayesian networks. In particular, we consider key features which support interactive behaviour: (1) an attentional mechanism which updates the activation of concepts as the interaction progresses; (2) a set of exploratory responses; and (3) a set of probabilistic patterns and an Argument Grammar which support the generation

2007

Formal logical tools are able to provide some amount of reasoning support for information analysis, but are unable to represent uncertainty. Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require specialized expertise to use effectively. We describe a framework for systems that incorporate the advantages of both Bayesian and logical systems. We define a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks. We then demonstrate that the merging of such networks with domain-specific causal models forms a consistent Bayesian network with correct values for the formulas derived in the proof. In particular, we show that hard evidential updates in which the premises of a proof are found to be true force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model. We provide several examples that demonstrate the generality of the natural deduction system by using inference schemas not supportable in Prolog.

1990

Comprehensible explanations of probabilistic reasoning are a prerequisite for wider acceptance of Bayesian methods in expert systems and decision support systems. A study of human reasoning under uncertainty suggests two different strategies for explaining probabilistic reasoning specially attuned to human thinking: The first, qualitative belief propagation, traces the qualitative effect of evidence through a belief network from one variable to the next. This propagation algorithm is an alternative to the graph reduction algorithms of Wellman (1988) for inference in qualitative probabilistic networks. It is based on a qualitative analysis of intercausal reasoning, which is a generalization of Pearl's "explaining away", and an alternative to Wellman's definition of qualitative synergy. The other, Scenario-based reasoning, involves the generation of alternative causal "stories" accounting for the evidence. Comparing a few of the most probable scenarios provides an approximate way to explain the results of probabilistic reasoning. Both schemes employ causal as well as probabilistic knowledge. Probabilities may be presented as phrases and/or numbers. Users can control the style, abstraction and completeness of explanations.

1998

Our argumentation system NAG uses Bayesian networks in a user model and in a normative model to assemble and assess nice arguments, that is arguments which balance persuasiveness with normative correctness. Attentional focus is simulated in both models to select relevant subnetworks for Bayesian propagation. Bayesian propagation in the user model is modified to represent some human cognitive weaknesses. The subnetworks are expanded in an iterative abductive process until argumentative goals are achieved in both models, when the argument is presented to the user.

2008

There is an escalating perception in some quarters that the conclusions drawn from digital evidence are the subjective views of individuals and have limited scientific justification. This paper attempts to address this problem by presenting a formal model for reasoning about digital evidence. A Bayesian network is used to quantify the evidential strengths of hypotheses and, thus, enhance the reliability and traceability of the results produced by digital forensic investigations. The validity of the model is tested using a real court case. The test uses objective probability assignments obtained by aggregating the responses of experienced law enforcement agents and analysts. The results confirmed the guilty verdict in the court case with a probability value of 92.7%.

1989

Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell -for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a. genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network componentsnodes and arcs-as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an exam ple of a real world application, MUN1N an expert system for electromyography is discussed.

2011

While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches to probabilistic logic fit into a simple unifying framework: logically complex evidence can be used to associate probability intervals or probabilities with sentences.