Weiru Liu

AAAI Conference 2026 Conference Paper

Fine-Grained Interpretation of Political Opinions in Large Language Models

• Jingyu Hu
• Mengyue Yang
• Mengnan Du
• Weiru Liu

Studies of LLMs’ political opinions mainly evaluate their open-ended responses. Recent work indicates misalignment between LLMs responses and their internal intentions. This motivates us to probe LLMs' internal mechanisms and uncover their internal political states. Additionally, analysis of LLMs' political opinions often relies on single-axis concepts, which can lead to concept confounds. Our work extends this to multi-dimensions and applies interpretable techniques for more transparent LLM political concept learning. Specifically, we designed a four-dimensional political learning framework and constructed a corresponding dataset for fine-grained political concept vector learning. These vectors can detect and intervene in LLM internals. Experiments are conducted on eight open-source LLMs with three representation engineering techniques. Results show these vectors can disentangle political concept confounds. Detection tasks validate the semantic meaning of the vectors and show good generalization and robustness in OOD settings. Intervention experiments show that these vectors can implicitly intervene in LLMs, generating responses with targeted political leanings. These insights reveal the need for more transparent auditing for future AI governance.

AAMAS Conference 2026 Conference Paper

Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts

• Hongbo Bo
• Jingyu Hu
• Weiru Liu

Large Language Models (LLMs) have emerged as a new paradigm for multi-agent systems. However, existing research on the behaviour of LLM-based multi-agents relies on ad hoc prompts and lacks a principled policy perspective. Different from reinforcement learning, we investigate whether prompt-as-action can be parameterized so as to construct a lightweight policy which consists of a sequence of state-action pairs to influence conversational behaviours without training. Our framework regards prompts as actions executed by LLMs, and dynamically constructs prompts through five components based on the current state of the agent. To test the effectiveness of parameterized control, we evaluated the dialogue flow based on five indicators: responsiveness, rebuttal, evidence usage, non-repetition, and stance shift. We conduct experiments using different LLM-driven agents in two discussion scenarios related to the general public and show that prompt parameterization can influence the dialogue dynamics. This result shows that policyparameterised prompts offer a simple and effective mechanism to influence the dialogue process, which will help the research of multi-agent systems in the direction of social simulation.

AAAI Conference 2025 Conference Paper

Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation

• Yiwei Shi
• Muning Wen
• Qi Zhang
• Weinan Zhang
• Cunjia Liu
• Weiru Liu

Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear feedback signals. For example, in a Source Term Estimation problem, the lack of precise environmental information makes it challenging to provide clear feedback signals and to define and evaluate how the source's location is determined. To address this challenge, the Autonomous Goal Detection and Cessation (AGDC) module was developed, enhancing various RL algorithms by incorporating a self-feedback mechanism for autonomous goal detection and cessation upon task completion. Our method effectively identifies and ceases undefined goals by approximating the agent's belief, significantly enhancing the capabilities of RL algorithms in environments with limited feedback. To validate effectiveness of our approach, we integrated AGDC with deep Q-Network, proximal policy optimization, and deep deterministic policy gradient algorithms, and evaluated its performance on the Source Term Estimation problem. The experimental results showed that AGDC-enhanced RL algorithms significantly outperformed traditional statistical methods such as infotaxis, entrotaxis, and dual control for exploitation and exploration, as well as a non-statistical random action selection method. These improvements were evident in terms of success rate, mean traveled distance, and search time, highlighting AGDC's effectiveness and efficiency in complex, real-world scenarios.

IROS Conference 2023 Conference Paper

TIMS: A Tactile Internet-Based Micromanipulation System with Haptic Guidance for Surgical Training

• Jialin Lin
• Xiaoqing Guo
• Wen Fan 0001
• Wei Li 0105
• Yuanyi Wang
• Jiaming Liang
• Jindong Liu
• Weiru Liu

Microsurgery involves the dexterous manipulation of delicate tissue or fragile structures, such as small blood vessels and nerves, under a microscope. To address the limitations of imprecise manipulation of human hands, robotic systems have been developed to assist surgeons in performing complex microsurgical tasks with greater precision and safety. However, the steep learning curve for robot-assisted microsurgery (RAMS) and the shortage of well-trained surgeons pose significant challenges to the widespread adoption of RAMS. Therefore, the development of a versatile training system for RAMS is necessary, which can bring tangible benefits to both surgeons and patients. In this paper, we present a Tactile Internet-Based Micromanipulation System (TIMS) based on a ROS-Django web-based architecture for microsurgical training. This system can provide tactile feedback to operators via a wearable tactile display (WTD), while real-time data is transmitted through the internet via a ROS-Django framework. In addition, TIMS integrates haptic guidance to ‘guide’ the trainees to follow a desired trajectory provided by expert surgeons. Learning from demonstration based on Gaussian Process Regression (GPR) was used to generate the desired trajectory. We conducted user studies to verify the effectiveness of our proposed TIMS, comparing users' performance with and without tactile feedback and/or haptic guidance. For more details of this project, please view our website: https://sites.google.com/view/viewtims/home.

AAAI Conference 2018 Conference Paper

Expected Utility with Relative Loss Reduction: A Unifying Decision Model for Resolving Four Well-Known Paradoxes

• Wenjun Ma
• Yuncheng Jiang
• Weiru Liu
• Xudong Luo
• Kevin McAreavey

Some well-known paradoxes in decision making (e. g. , the Allais paradox, the St. Petersburg paradox, the Ellsberg paradox, and the Machina paradox) reveal that choices conventional expected utility theory predicts could be inconsistent with empirical observations. So, solutions to these paradoxes can help us better understand humans decision making accurately. This is also highly related to the prediction power of a decision-making model in real-world applications. Thus, various models have been proposed to address these paradoxes. However, most of them can only solve parts of the paradoxes, and for doing so some of them have to rely on the parameter tuning without proper justifications for such bounds of parameters. To this end, this paper proposes a new descriptive decision-making model, expected utility with relative loss reduction, which can exhibit the same qualitative behaviours as those observed in experiments of these paradoxes without any additional parameter setting. In particular, we introduce the concept of relative loss reduction to reflect people’s tendency to prefer ensuring a sufficient minimum loss to just a maximum expected utility in decision-making under risk or ambiguity.

JAIR Journal 2017 Journal Article

Managing Different Sources of Uncertainty in a BDI Framework in a Principled Way with Tractable Fragments

• Kim Bauters
• Kevin McAreavey
• Weiru Liu
• Jun Hong
• Lluís Godo
• Carles Sierra

The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents - or components - each one modelled based on its beliefs, desires and intentions. However, current BDI implementations are not well-suited for modelling more realistic intelligent systems which operate in environments pervaded by different types of uncertainty. Furthermore, existing approaches for dealing with uncertainty typically do not offer syntactical or tractable ways of reasoning about uncertainty. This complicates their integration with BDI implementations, which heavily rely on fast and reactive decisions. In this paper, we advance the state-of-the-art w.r.t. handling different types of uncertainty in BDI agents. The contributions of this paper are, first, a new way of modelling the beliefs of an agent as a set of epistemic states. Each epistemic state can use a distinct underlying uncertainty theory and revision strategy, and commensurability between epistemic states is achieved through a stratification approach. Second, we present a novel syntactic approach to revising beliefs given unreliable input. We prove that this syntactic approach agrees with the semantic definition, and we identify expressive fragments that are particularly useful for resource-bounded agents. Third, we introduce full operational semantics that extend CAN, a popular semantics for BDI, to establish how reasoning about uncertainty can be tightly integrated into the BDI framework. Fourth, we provide comprehensive experimental results to highlight the usefulness and feasibility of our approach, and explain how the generic epistemic state can be instantiated into various representations.

AAMAS Conference 2017 Conference Paper

The Event Calculus in Probabilistic Logic Programming with Annotated Disjunctions

• Kevin McAreavey
• Kim Bauters
• Weiru Liu
• Jun Hong

We propose a new probabilistic extension to the event calculus using the probabilistic logic programming (PLP) language ProbLog, and a language construct called the annotated disjunction. This is the first extension of the event calculus capable of handling numerous sources of uncertainty (e. g. from primitive event observations and from composite event definitions). It is also the first extension capable of handling multiple sources of event observations (e. g. in multi-sensor environments). We describe characteristics of this new extension (e. g. rationality of conclusions), and prove some important properties (e. g. validity in ProbLog). Our extension is directly implementable in ProbLog, and we successfully apply it to the problem of activity recognition under uncertainty in an event detection data set obtained from vision analytics of bus surveillance video.

ECAI Conference 2014 Conference Paper

An Intelligent Threat Prevention Framework with Heterogeneous Information

• Wenjun Ma
• Weiru Liu

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects' intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e. g. , the dispatch of security team) to prevent a suspect's further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

UAI Conference 2014 Conference Paper

CAN(PLAN)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information

• Kim Bauters
• Weiru Liu
• Jun Hong 0001
• Carles Sierra
• Lluís Godo

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, provides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisition (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of CAN(PLAN) into CAN(PLAN)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent’s beliefs. These epistemic states are stratified to make them commensurable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient semantics. Finally, we examine how primitive actions are affected by uncertainty and we define an appropriate form of lookahead planning.

ECAI Conference 2014 Conference Paper

Video Event Recognition by Dempster-Shafer Theory

• Xin Hong
• Yan Huang
• Wenjun Ma
• Paul Miller 0003
• Weiru Liu
• Huiyu Zhou 0001

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high-level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

IJCAI Conference 2013 Conference Paper

An Ambiguity Aversion Framework of Security Game under Ambiguities

• Wenjun Ma
• Xudong Luo
• Weiru Liu

Security is a critical concern around the world. Since resources for security are always limited, lots of interest have arisen in using game theory to handle security resource allocation problems. However, most of the existing work does not address adequately how a defender chooses his optimal strategy in a game with absent, inaccurate, uncertain, and even ambiguous strategy profiles’ payoffs. To address this issue, we propose a general framework of security games under ambiguities based on Dempster-Shafer theory and the ambiguity aversion principle of minimax regret. Then, we reveal some properties of this framework. Also, we present two methods to reduce the influence of complete ignorance. Our investigation shows that this new framework is better in handling security resource allocation problems under ambiguities.

KR Conference 2012 Short Paper

Revising partial pre-orders with partial pre-orders: A unit-based revision framework

• Jianbing Ma
• Salem Benferhat
• Weiru Liu

(Benferhat, Lagrue, and Papini 2005)). In (Benferhat et al. 2000), the epistemic state, representing initial information, and the input, representing new information, are both total pre-orders. In (Benferhat, Lagrue, and Papini 2005), the initial epistemic state is indeed a partial pre-order, however, the input information is a propositional formula. In (Bochman 2001), different strategies have been proposed to revise an epistemic state represented by a partial pre-order on the possible worlds. However, in this book there are no revision methods for revising a partial pre-order by a partial pre-order. Our revision operations are also totally different from Lang’s works on preference (e. g. (Lang and van der Torre 2008)), and Weydert, Freund and Kern-Isberner’s revision with conditionals (e. g., (Weydert 1994; Freund 1998; Kern-Isberner 2002)). So far in the literature, there is hardly any work that studies the revision of an epistemic state (especially a partial pre-order) being revised by a partial preorder (a new input). The only work we have seen addressing this issue is a recent paper (Tamargo et al. 2011), in which revision of partial orders is studied in a standard expansion and contraction way. But it does not provide concrete revision results because of the use of certain kinds of selection functions. In this paper, we investigate revision strategies for this setting: a partial pre-order revised by another partial preorder. With this perspective, each individual ordering relation (a pair of elements with an ordering connective), which we name unit, contained in the input is itself an important piece of evidence that should be preserved (Ma, Liu, and Hunter 2011). To propose a revision framework for partial pre-orders, we investigate how a revision operator should be designed. Generally speaking, both a priori ordering set, S, and a new input SI can be seen as sets containing individual ordering relations, e. g., the units. So, revision can be carried out by (i) deriving maximal supersets of SI that contain suitable units in S which do not lead to possible contradiction; (ii) by inserting units from SI to S while removing any units that are inconsistent with this insertion; or (iii) by enlarging SI through inserting one unit from S at a time, while maintaining consistency, etc. Based on these intuitions, we propose a family of unit-based revision operators, dubbed extension revision, match revision, inner revision, and outer revision. We prove the equivalence between these operators Belief revision studies strategies about how agents revise their belief states when receiving new evidence. Both in classical belief revision and in epistemic revision, a new input is either in the form of a (weighted) propositional formula or a total pre-order (where the total pre-order is considered as a whole). However, in some real-world applications, a new input can be a partial pre-order where each unit that constitutes the partial pre-order is important and should be considered individually. To address this issue, in this paper, we study how a partial preorder representing the prior epistemic state can be revised by another partial pre-order (the new input) from a different perspective, where the revision is conducted recursively on the individual units of partial pre-orders. We propose different revision operators (rules), dubbed the extension, match, inner and outer revision operators, from different revision points of view. We also analyze several properties for these operators.

UAI Conference 2011 Conference Paper

Belief change with noisy sensing in the situation calculus

• Jianbing Ma
• Weiru Liu
• Paul Miller 0003

AAAI Conference 2010 Conference Paper

A Belief Revision Framework for Revising Epistemic States with Partial Epistemic States

• Jianbing Ma
• Weiru Liu
• Salem Benferhat

Belief revision performs belief change on an agent’s beliefs when new evidence (either of the form of a propositional formula or of the form of a total pre-order on a set of interpretations) is received. Jeffrey’s rule is commonly used for revising probabilistic epistemic states when new information is probabilistically uncertain. In this paper, we propose a general epistemic revision framework where new evidence is of the form of a partial epistemic state. Our framework extends Jeffrey’s rule with uncertain inputs and covers wellknown existing frameworks such as ordinal conditional function (OCF) or possibility theory. We then define a set of postulates that such revision operators shall satisfy and establish representation theorems to characterize those postulates. We show that these postulates reveal common characteristics of various existing revision strategies and are satisfied by OCF conditionalization, Jeffrey’s rule of conditioning and possibility conditionalization. Furthermore, when reducing to the belief revision situation, our postulates can induce most of Darwiche and Pearl’s postulates.

KER Journal 2010 Journal Article

A survey of formalisms for representing and reasoning with scientific knowledge

• Anthony Hunter
• Weiru Liu

Abstract With the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.

AAAI Conference 2010 Conference Paper

Inducing Probability Distributions from Knowledge Bases with (In)dependence Relations

• Jianbing Ma
• Weiru Liu
• Anthony Hunter

When merging belief sets from different agents, the result is normally a consistent belief set in which the inconsistency between the original sources is not represented. As probability theory is widely used to represent uncertainty, an interesting question therefore is whether it is possible to induce a probability distribution when merging belief sets. To this end, we first propose two approaches to inducing a probability distribution on a set of possible worlds, by extending the principle of indifference on possible worlds. We then study how the (in)dependence relations between atoms can influence the probability distribution. We also propose a set of properties to regulate the merging of belief sets when a probability distribution is output. Furthermore, our merging operators satisfy the well known Konieczny and Pino-Pérez postulates if we use the set of possible worlds which have the maximal induced probability values. Our study shows that taking an induced probability distribution as a merging result can better reflect uncertainty and inconsistency among the original knowledge bases.

UAI Conference 2010 Conference Paper

Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation

• Guilin Qi
• Jianfeng Du
• Weiru Liu
• David A. Bell

Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and dealing with uncertainty. They often result in a possibilistic knowledge base which is a set of weighted formulas. Although possibilistic logic is inconsistency tolerant, it suffers from the well-known “drowning effect”. Therefore, we may still want to obtain a consistent possibilistic knowledge base as the result of merging. In such a case, we argue that it is not always necessary to keep weighted information after merging. In this paper, we define a merging operator that maps a set of possibilistic knowledge bases and a formula representing the integrity constraints to a classical knowledge base by using lexicographic ordering. We show that it satisfies nine postulates that generalize basic postulates for propositional merging given in [11]. These postulates capture the principle of minimal change in some sense. We then provide an algorithm for generating the resulting knowledge base of our merging operator. Finally, we discuss the compatibility of our merging operator with propositional merging and establish the advantage of our merging operator over existing semantic merging operators in the propositional case.

IJCAI Conference 2009 Conference Paper

• Anbu Yue
• Weiru Liu

In this paper, we address the problem of merging multiple imprecise probabilistic beliefs represented as Probabilistic Logic Programs (PLPs) obtained from multiple sources. Beliefs in each PLP are modeled as conditional events attached with probability bounds. The major task of syntax-based merging is to obtain the most rational probability bound for each conditional event from the original PLPs to form a new PLP. We require the minimal change principle to be followed so that each source gives up its beliefs as little as possible. Some instantiated merging operators are derived from our merging framework. Furthermore, we propose a set of postulates for merging PLPs, some of which extend the postulates for merging classical knowledge bases, whilst others are specific to the merging of probabilistic beliefs.

ECAI Conference 2008 Conference Paper

A General Model for Epistemic State Revision using Plausibility Measures

• Jianbing Ma
• Weiru Liu

In this paper, we present a general revision model on epistemic states based on plausibility measures proposed by Friedman and Halpern. We propose our revision strategy and give some desirable properties, e. g. , the reversible and commutative properties. Moreover, we develop a notion called plausibility kinematics and show that our revision strategy follows plausibility kinematics. Furthermore, we prove that the revision following plausibility kinematics satisfies the principle of minimal change based on some distance measures. Finally, we discuss a revision operator defined for plausibility functions and its relationship with iterated belief revision proposed by Darwiche and Pearl. We show that the revision operator satisfies all the DP postulates when it is Max-Additive.

ECAI Conference 2008 Conference Paper

Belief Revision through Forgetting Conditionals in Conditional Probabilistic Logic Programs

• Anbu Yue
• Weiru Liu

In this paper, we present a revision strategy of revising a conditional probabilistic logic program (PLP) when new information is received (which is in the form of probabilistic formulae), through the technique of variable forgetting. We first extend the traditional forgetting method to forget a conditional event in PLPs. We then propose two revision operators to revise a PLP based on our forgetting method. By revision through forgetting, the irrelevant knowledge in the original PLP is retained according to the minimal change principle. We prove that our revision operators satisfy most of the postulates for probabilistic belief revision. A main advantage of our revision operators is that a new PLP is explicitly obtained after revision, since our revision operator performs forgetting a conditional event at the syntax level.

AIJ Journal 2006 Journal Article

Analyzing the degree of conflict among belief functions

• Weiru Liu

The study of alternative combination rules in DS theory when evidence is in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. These studies have mainly focused on investigating which alternative would be appropriate for which conflicting situation, under the assumption that a conflict is identified. The issue of detection (or identification) of conflict among evidence has been ignored. In this paper, we formally define when two basic belief assignments are in conflict. This definition deploys quantitative measures of both the mass of the combined belief assigned to the emptyset before normalization and the distance between betting commitments of beliefs. We argue that only when both measures are high, it is safe to say the evidence is in conflict. This definition can be served as a prerequisite for selecting appropriate combination rules.

JELIA Conference 2006 Conference Paper

Knowledge Base Revision in Description Logics

• Guilin Qi
• Weiru Liu
• David A. Bell

Abstract Ontology evolution is an important problem in the Semantic Web research. Recently, Alchourrón, Gärdenfors and Markinson’s (AGM) theory on belief change has been applied to deal with this problem. However, most of current work only focuses on the feasibility of the application of AGM postulates on contraction to description logics (DLs), a family of ontology languages. So the explicit construction of a revision operator is ignored. In this paper, we first generalize the AGM postulates on revision to DLs. We then define two revision operators in DLs. One is the weakening-based revision operator which is defined by weakening of statements in a DL knowledge base and the other is its refinement. We show that both operators capture some notions of minimal change and satisfy the generalized AGM postulates for revision.

UAI Conference 2005 Conference Paper

A Revision-Based Approach to Resolving Conflicting Information

• Guilin Qi
• Weiru Liu
• David A. Bell

In this paper, we propose a revision-based approach for conflict resolution by generalizing the Disjunctive Maxi-Adjustment (DMA) approach (Benferhat et al. 2004). Revision operators can be classified into two different families: the model-based ones and the formula-based ones. So the revision-based approach has two different versions according to which family of revision operators is chosen. Two particular revision operators are considered, one is the Dalal's revision operator, which is a model-based revision operator, and the other is the cardinality-maximal based revision operator, which is a formulabased revision operator. When the Dalal's revision operator is chosen, the revision-based approach is independent of the syntactic form in each stratum and it captures some notion of minimal change. When the cardinalitymaximal based revision operator is chosen, the revision-based approach is equivalent to the DMA approach. We also show that both approaches are computationally easier than the DMA approach.

IJCAI Conference 2005 Conference Paper

Measuring conflict and agreement between two prioritized belief bases

• Guilin Qi
• Weiru Liu
• David A

In this paper, we investigate the relationship between two prioritized knowledge bases by measuring both the conflict and the agreement between them. First of all, a quantity of conflict and two quantities of agreement are defined. The former is shown to be a generalization of the Dalal distance. The latter are, respectively, a quantity of strong agreement which measures the amount of information on which two belief bases “totally” agree, and a quantity of weak agreement which measures the amount of information that is believed by one source but is unknown to the other. All three quantity measures are based on the weighted prime implicant, which represents beliefs in a prioritized belief base. We then define a degree of conflict and two degrees of agreement based on our quantity of conflict and the quantities of agreement. We also consider the impact of these measures on belief merging and information source ordering.

KR Conference 2004 Conference Paper

A split-combination method for merging inconsistent knowledge bases in possibilistic logic

• David Glass
• Qi Guilin
• Weiru Liu

In this paper, a new method for merging multiple inconsistent knowledge bases in the framework of possibilistic logic is presented. We divide the fusion process into two steps: one is called the splitting step and the other is called the combination step. Given several inconsistent possibilistic knowledge bases (i. e. the union of these possibilistic bases is inconsistent), we split each of them into two subbases according to the upper free degree of their union, such that one subbase contains formulas whose necessity degrees are less than the upper free degree and the other contains formulas whose necessity degrees are greater than the upper free degree. In the second step, we combine the former using the maximum (or more generally, T-conorm) combination mode, while combining the latter using the minimum (or more generally, T-norm) combination mode. The union of the possibilistic bases obtained by the second step is taken as the final result of the combination of the possibilistic bases that we want to merge. We prove that when the possibilistic bases are consistent with each other, the result of our new combination method is equivalent to that of the minimum (T-norm) based combination mode. However, when the sources are inconsistent with each other, the result of our combination mode is better than that obtained by using the maximum (T-conorm) based mode. An alternative approach to splitting the possibilistic bases is introduced in the last section. The combination mode obtained by this splitting method can be applied to combine knowledge bases which are flat, i. e., without any priority between their elements.

NMR Workshop 2004 Conference Paper

Combining individually inconsistent prioritized knowledge bases

• Guilin Qi
• Weiru Liu
• David H. Glass

It is well accepted that inconsistency may exist in a database system or an intelligent information system (Benferhat et al. 1993a; 1993b; 1997b; 1998; Benferhat & Kaci 2003; Elvang-Gøransson & Hunter 1995; Gabbay & Hunter 1991; Lin 1994; Priest et al. 1989; Priest 2001). Inconsistency can either appear in the given knowledge bases or as a result of combination or revision. In this paper, we will propose two different methods to combine individually inconsistent possibilistic knowledge bases. The first method, called an argument-based method, is a generalization of the merging method introduced in (Benferhat & Kaci 2003). When the knowledge bases to be merged are self-consistent, this method coincides with the original one. The second method, called a multiple-operator based method, combines the consistent and the conflict information using different operators. This method is more reasonable than the argument-based method because it differentiates the consistent and conflict information.

AIJ Journal 2002 Journal Article

Learning Bayesian networks from data: An information-theory based approach

• Jie Cheng
• Russell Greiner
• Jonathan Kelly
• David Bell
• Weiru Liu

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

UAI Conference 1992 Conference Paper

Representing Heuristic Knowledge in D-S Theory

• Weiru Liu
• John G. Hughes
• Michael F. McTear