Designing Intelligent Expert Systems to Cope with Liars

To cope with the problem of input distortion by users of Web-based expert systems, we develop methods to distinguish liars from truth-tellers based on verifiable attributes, and redesign the expert systems to control the impact of input distortion. The four methods we propose are termed split tree, consolidated tree, value based split tree, and value based consolidated tree. They improve the performance of expert systems by improving accuracy or reduce misclassification cost. Numerical examples confirm that the most possible accurate recommendation is not always the most economical one. The recommendations based on minimizing misclassification costs are more moderate compared to that based on accuracy. In addition, the consolidated tree methods are more efficient than the split tree methods, since they do not always require the verification of attribute values

Logic and lattices for a statistics advisor

The work partially reported here concerned the development ot a prototype Expert System for giving advice about Statistics experiments, called ASA, and an inference engine to support ASA, called ABASE.This involved discovering what knowledge was necessary for performing the task at a satis¬ factory level of competence, working out how to represent this knowledge in a computer, and how to process the representations efficiently.Two areas of Statistical knowledge are described in detail: the classification of measure¬ ments and statistical variables, and the structure of elementary statistical experiments. A knowledge representation system based on lattices is proposed, and it is shown that such representations are learnable by computer programs, and lend themselves to particularly efficient implementation.ABASE was influenced by MBASE, the inference engine of MECHO [Bundy et al 79a]. Both are theorem provers working on typed function-free Horn clauses, with controlled creation of new entities. Their type systems and proof procedures are radically different, though, and ABASE is "conversational" while MBASE is not

Providing adaptive health updates across the personal social network

Peer reviewedPublisher PD

Promoting Honesty in Electronic Marketplaces: Combining Trust Modeling and Incentive Mechanism Design

This thesis work is in the area of modeling trust in multi-agent systems, systems of software agents designed to act on behalf of users (buyers and sellers), in applications such as e-commerce. The focus is on developing an approach for buyers to model the trustworthiness of sellers in order to make effective decisions about which sellers to select for business. One challenge is the problem of unfair ratings, which arises when modeling the trust of sellers relies on ratings provided by other buyers (called advisors). Existing approaches for coping with this problem fail in scenarios where the majority of advisors are dishonest, buyers do not have much personal experience with sellers, advisors try to flood the trust modeling system with unfair ratings, and sellers vary their behavior widely. We propose a novel personalized approach for effectively modeling trustworthiness of advisors, allowing a buyer to 1) model the private reputation of an advisor based on their ratings for commonly rated sellers 2) model the public reputation of the advisor based on all ratings for the sellers ever rated by that agent 3) flexibly weight the private and public reputation into one combined measure of the trustworthiness of the advisor. Our approach tracks ratings provided according to their time windows and limits the ratings accepted, in order to cope with advisors flooding the system and to deal with changes in agents' behavior. Experimental evidence demonstrates that our model outperforms other models in detecting dishonest advisors and is able to assist buyers to gain the largest profit when doing business with sellers. Equipped with this richer method for modeling trustworthiness of advisors, we then embed this reasoning into a novel trust-based incentive mechanism to encourage agents to be honest. In this mechanism, buyers select the most trustworthy advisors as their neighbors from which they can ask advice about sellers, forming a social network. In contrast with other researchers, we also have sellers model the reputation of buyers. Sellers will offer better rewards to satisfy buyers that are well respected in the social network, in order to build their own reputation. We provide precise formulae used by sellers when reasoning about immediate and future profit to determine their bidding behavior and the rewards to buyers, and emphasize the importance for buyers to adopt a strategy to limit the number of sellers that are considered for each good to be purchased. We theoretically prove that our mechanism promotes honesty from buyers in reporting seller ratings, and honesty from sellers in delivering products as promised. We also provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. This provides a stronger defense of the mechanism as one that is robust to important conditions in the marketplace. Our experiments clearly show the gains in profit enjoyed by both honest sellers and honest buyers when our mechanism is introduced and our proposed strategies are followed. In general, our research will serve to promote honesty amongst buyers and sellers in e-marketplaces. Our particular proposal of allowing sellers to model buyers opens a new direction in trust modeling research. The novel direction of designing an incentive mechanism based on trust modeling and using this mechanism to further help trust modeling by diminishing the problem of unfair ratings will hope to bridge researchers in the areas of trust modeling and mechanism design

How Linguistic Frames Affect Motivational Profiles and the Roles of Quantitative versus Qualitative Research Strategies

The combined tools of psycholinguistics and systems analysis have produced advances in motivational profiling resulting in numerous applications to behavioral engineering. Knowing the way people frame their motive offers leverage in causing behavior change ranging from persuasive marketing campaigns, forensic profiling, individual psychotherapy, and executive performance. Professionals study motivation in applied or theoretical settings, often with strong implicit biases toward either quantitative or qualitative strategies. Many experts habitually frame behavioral research issues with ill-fitting quantitative and qualitative strategies. The third strategic choice offered here is state-of -the -art, psycholinguistic communications modeling. The role of these research strategies is explored

Terrorism - Lessons from Natural and Human Co-evolutionary Arms Races

The potential for violent, destructive and threatening behaviour against fellow humans seems to be inherent in our psychological and anatomical nature (the latter arguably evidenced by special adaptations of our fists, and fist-resistant faces (Morgan and Carrier, 2013; Carrier and Morgan, 2014)). This is true whether that behaviour concerns an argument over an insult, careless driving, domestic relations, religious beliefs and practices, or who governs Eastern Ukraine. The kinds of tactics and strategies classed, at times, as acts of terrorism, fall within this set. Fortunately equally inherent in us are cooperation, empathy and altruism, although in a conflict these can be selectively applied to one’s own side, even by terrorists. We can look to our evolutionary origins to help understand, and hopefully to influence, what turns us – as individuals and groups – on and off violent conflict, who we target, over what moral/political causes and under what circumstances. This is the domain of beliefs, identities, ideologies and motivation. But we can also take another perspective, which is how conflicts tactically and strategically unfold, and how this process can be influenced for the differential benefit of the ‘good side’. The ‘how’ essentially concerns the process of adaptation, whereby organisms as individuals, groups or species change over some relevant timescale to become better fitted to survival, flourishing and reproduction in their habitual environment. Adaptation for potentially violent and destructive conflict such as carrying out terrorism or defending against it is the core concern of this chapter, although adaptation for cooperation and straightforward foraging with or without violence also play a part. The aim of the chapter as a whole is to explore the lessons for counter-terrorism from evolutionary studies of adaptation in both human and natural domains, with special focus on arms races. This is partly to come up with some practical suggestions at tactical and strategic levels; but partly also to foster a distinctive and, I will argue, promising way of thinking among policymakers, security services, engineers, planners and designers. I show in particular how evolutionary processes apply to cultural (including technological) change, opening the knowledge-transfer process up to a range of natural, and human, co-evolutionary struggles. Following that, I show how such a widened perspective can apply to terrorism and counterterrorism in particular. Before concluding, I discuss a range of lessons for how to run terrorist arms races, drawing heavily on those most human of culturally-evolved adaptive processes, design, research, theory and evaluation

The specificity of skill acquisition: Is it task related?

The plethora of research into the area of skill acquisition and transfer has resulted in conflicting conclusions regarding the nature of transfer. Some researchers have found skill transfer to be specific to the items experienced during training (Logan, I 988, alphabet-arithmetic task; Masson, 1986, reverse reading task). Others have found transfer to be general (Speelman & Kirsner, I 997, syllogism task) or both general and specific in the same task (Greig & Speelman, 1999, algebra task). This study investigated the assumption that the task involved dictates the specific nature of skill acquisition and transfer. Sixty participants drawn from the Edith Cowan School of Psychology volunteer register were randomly assigned to four groups, with each group performing one of the afore mentioned tasks. In phase 1, learning was determined by the decreased Reaction Time (RT) for each participant from block 1 to block 8. Phase 2 involved participants being trained on a different task using one set of items and then in the transfer phase (3) participants performed the same task but with new items. Comparing RT data from block 1 phase 2 and block 1 phase 3 and from block 1 phase 3 to block 10 phase 2 assessed transfer. The syllogism task resulted in the most skill transfer due to the generalisability of the strategy employed in solving the syllogisms. This was followed by the algebra task, the alphabet-arithmetic task, and the reversed reading task. The results confirmed the a priori predictions that the nature of transfer is a function of the task involved