Maximizing Expected Impact in an Agent Reputation Network

Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observ- able Markov decision process (POMDP) do not take the benevolence of other agents for granted. We propose a new POMDP-based framework which is general enough for the specification of a variety of stochastic MAS domains involving the impact of agents on each other’s reputa- tions. A unique feature of this framework is that actions are specified as either undirected (regular) or directed (towards a particular agent), and a new directed transition function is provided for modeling the effects of reputation in interactions. Assuming that an agent must maintain a good enough reputation to survive in the network, a planning algorithm is developed for an agent to select optimal actions in stochastic MASs. Preliminary evaluation is provided via an example specification and by determining the algorithm’s complexity

Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders

Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers

A Modal Logic for the Decision-Theoretic Projection Problem

We present a decidable logic in which queries can be posed about (i) the degree of belief in a propositional sentence after an arbitrary finite number of actions and observations and (ii) the utility of a finite sequence of actions after a number of actions and observations. Another contribution of this work is that a POMDP model specification is allowed to be partial or incomplete with no restriction on the lack of information specified for the model. The model may even contain information about non-initial beliefs. Essentially, entailment of arbitrary queries (expressible in the language) can be answered. A sound, complete and terminating decision procedure is provided

Defeasible Entailment: from Rational Closure to Lexicographic Closure and Beyond

In this paper we present what we believe to be the first systematic approach for extending the framework for de- feasible entailment first presented by Kraus, Lehmann, and Magidor—the so-called KLM approach. Drawing on the properties for KLM, we first propose a class of basic defea- sible entailment relations. We characterise this basic frame- work in three ways: (i) semantically, (ii) in terms of a class of properties, and (iii) in terms of ranks on statements in a knowlege base. We also provide an algorithm for computing the basic framework. These results are proved through vari- ous representation results. We then refine this framework by defining the class of rational defeasible entailment relations. This refined framework is also characterised in thee ways: se- mantically, in terms of a class of properties, and in terms of ranks on statements. We also provide an algorithm for com- puting the refined framework. Again, these results are proved through various representation results. We argue that the class of rational defeasible entail- ment relations—a strengthening of basic defeasible entail- ment which is itself a strengthening of the original KLM proposal—is worthy of the term rational in the sense that all of them can be viewed as appropriate forms of defeasi- ble entailment. We show that the two well-known forms of defeasible entailment, rational closure and lexicographic clo- sure, fall within our rational defeasible framework. We show that rational closure is the most conservative of the defeasi- ble entailment relations within the framework (with respect to subset inclusion), but that there are forms of defeasible en- tailment within our framework that are more “adventurous” than lexicographic closure

The Bayesian Description Logic BALC

Description Logics (DLs) that support uncertainty are not as well studied as their crisp alternatives, thereby limiting their use in real world domains. The Bayesian DL BEL and its extensions have been introduced to deal with uncertain knowledge without assuming (prob- abilistic) independence between axioms. In this paper we combine the classical DL ALC with Bayesian Networks. Our new DL includes a so- lution to the consistency checking problem and changes to the tableaux algorithm that are not a part of BEL. Furthermore, BALC also supports probabilistic assertional information which was not studied for BEL. We present algorithms for four categories of reasoning problems for our logic; two versions of concept satisfiability (referred to as total concept satis- fiability and partial concept satisfiability respectively), knowledge base consistency, subsumption, and instance checking. We show that all rea- soning problems in BALC are in the same complexity class as their classical variants, provided that the size of the Bayesian Network is included in the size of the knowledge base

A polynomial Time Subsumption Algorithm for Nominal Safe ELO_bot under Rational Closure

Description Logics (DLs) under Rational Closure (RC) is a well-known framework for non-monotonic reasoning in DLs. In this paper, we address the concept subsumption decision problem under RC for nominal safe ELO_bot, a notable and practically important DL representative of the OWL 2 profile OWL 2 EL. Our contribution here is to define a polynomial time subsumption procedure for nominal safe ELO_bot under RC that relies entirely on a series of classical, monotonic EL_bot subsumption tests. Therefore, any existing classical monotonic EL_bot reasoner can be used as a black box to implement our method. We then also adapt the method to one of the known extensions of RC for DLs, namely Defeasible Inheritance-based DLs without losing the computational tractability

Ontology-driven taxonomic work ows for Afrotropical Bees

This poster presents the results of an investigation into the use of ontology technologies to support taxonomy functions. Taxonomy is the science of naming and grouping biological organisms into a hierarchy. A core function of biological taxonomy is the classi cation and revised classi cation of biological organisms into an agreed upon taxonomic structure based on sets of shared characteristics. Recent developments in knowledge representation within Computer Science include the establishment of computational ontologies. Such ontologies are particularly well suited to support classi cation functions such as those used in biological taxonomy. Using a speci c genus of Afrotropical bees, this research project captured and represented the taxonomic knowledge base into an OWL2 ontology. In addition, the project used and extended available reasoning algorithms over the ontology to draw inferences that support the necessary taxonomy functions, and developed an application, the web ontology classi er (WOC). The WOC uses the Afrotropical bee ontology and demonstrates the taxonomic functions namely: identi cation (keys) as well as the description and comparison of taxa (taxonomic revision)

Using Defeasible Information to Obtain Coherence

We consider the problem of obtaining coherence in a propositional knowledge base using techniques from Belief Change. Our motivation comes from the field of formal ontologies where coherence is interpreted to mean that a concept name has to be satisfiable. In the propositional case we consider here, this translates to a propositional formula being satisfiable. We define be- lief change operators in a framework of nonmonotonic preferential reasoning. We show how the introduction of defeasible information using contraction operators can be an effective means for obtaining coherence

A New Approach to Probabilistic Belief Change

One way for an agent to deal with uncertainty about its beliefs is to maintain a probability distribution over the worlds it believes are possible. A belief change operation may recommend some previously believed worlds to become impossible and some previously disbelieved worlds to become possible. This work investigates how to redistribute probabilities due to worlds being added to and removed from an agent’s belief-state. Two related approaches are proposed and analyzed

Semantic Technologies and Big Data Analytics for Cyber Defence

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed