In a standard possibilistic logic, prioritized information are encoded by means of weighted knowledge base. This paper proposes an extension of possibilistic logic for dealing with partially ordered information. We Show that all basic notions of standard possibilitic logic (sumbsumption, syntactic and semantic inference, etc.) have natural couterparts when dealing with partially ordered information. We also propose an algorithm which computes possibilistic conclusions of a partial knowledge base of a partially ordered knowlege base.

Neurules are a type of neuro-symbolic rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Neurules exhibit characteristics such as modularity, naturalness and ability to perform interactive and integrated inferences. One way of producing a neurule base is through conversion of an existing symbolic rule base yielding an equivalent but more compact rule base. The conversion process merges symbolic rules having the same conclusion into one or more neurules. Due to the inability of the adaline unit to handle inseparability, more than one neurule for each conclusion may be produced. In this paper, we define criteria concerning the ability or inability to convert a rule set into a single neurule. Definition of criteria determining whether a set of symbolic rules can (or cannot) be converted into a single, equivalent but more compact rule is of general representational interest. With application of such criteria, the conversion process of symbolic rules into neurules becomes more time- and space-efficient by omitting useless trainings. Experimental results are promising.

Artificial intelligence (AI) researchers continue to face large challenges in their quest to develop truly intelligent systems. Topics of interest at the workshop include the representation of symbolic knowledge by connectionist systems; integrated neural-symbolic learning approaches; extraction of symbolic knowledge from trained neural networks; integrated neural-symbolic reasoning; biologically-inspired neural-symbolic integration; integration of logic and probabilities in neural networks; structured learning and relational learning in neural networks; applications in robotics, simulation, fraud prevention, semantic web, soware engineering, fault diagnosis, bioinformatics, visual intelligence, and so on.

In this paper, we investigate belief revision in possibilistic logic, which is a weighted logic proposed to deal with incomplete and uncertain information. Existing revision operators in possibilistic logic are restricted in the sense that the input information can only be a formula instead of a possibilistic knowledge base which is a set of weighted formulas. To break this restriction, we consider weighted prime implicants of a possibilistic knowledge base and use them to define novel revision operators in possibilistic logic. Intuitively, a weighted prime implicant of a possibilistic knowledge base is a logically weakest possibilistic term (i.e., a set of weighted literals) that can entail the knowledge base. We first show that the existing definition of a weighted prime implicant is problematic and need a modification. To define a revision operator using weighted prime implicants, we face two problems. The first problem is that we need to define the notion of a conflict set between two weighted prime implicants of two possibilistic knowledge bases to achieve minimal change. The second problem is that we need to define the disjunction of possibilistic terms. We solve these problems and define two conflict-based revision operators in possibilistic logic. We then adapt the well-known postulates for revision proposed by Katsuno and Mendelzon and show that our revision operators satisfy four of the basic adapted postulates and satisfy two others in some special cases.

While founded on the situation calculus, current implementations of Golog are mainly based on the closed-world assumption or its dynamic versions or the domain closure assumption. Also, they are almost exclusively based on regression. In this paper, we propose a first-order interpreter for knowledge-based Golog with sensing based on exact progression and limited reasoning. We assume infinitely many unique names and handle first-order disjunctive information in the form of the so-called proper+ KBs. Our implementation is based on the progression and limited reasoning algorithms for proper+ KBs proposed by Liu, Lakemeyer and Levesque. To improve efficiency, we implement the two algorithms by grounding via a trick based on the unique name assumption. The interpreter is online but the programmer can use two operators to specify offline execution for parts of programs. The search operator returns a conditional plan, while the planning operator is used when local closed-world information is available and calls a modern planner to generate a sequence of actions.

In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.

Current Coreference Resolution systems utilize a broad range of general knowledge features to make resolutions in a general setting. These approaches ignore coreference knowledge found in domain specific collections and how coreferent entities interact in different domains. This research addresses these issues by developing knowledge bases of coreference characteristics drawn from annotated and unannotated domain texts and utilizing lexical and discourse information to improve resolution.

The task of automatically determining the correct sense of a polysemous word has remained a challenge to this day. In our research, we introduce Concept-Based Disambiguation (CBD), a novel framework that utilizes recent semantic analysis techniques to represent both the context of the word and its senses in a high-dimensional space of natural concepts. The concepts are retrieved from a vast encyclopedic resource, thus enriching the disambiguation process with large amounts of domain-specific knowledge. In such concept-based spaces, more comprehensive measures can be applied in order to pick the right sense. Additionally, we introduce a novel representation scheme, denoted anchored representation, that builds a more specific text representation associated with an anchoring word. We evaluate our framework and show that the anchored representation is more suitable to the task of word-sense disambiguation (WSD). Additionally, we show that our system is superior to state-of-the-art methods when evaluated on domain-specific corpora, and competitive with recent methods when evaluated on a general corpus.

How do reasoning systems that learn evolve over time? What are the properties of different learning strategies? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how large knowledge-based systems evolve: Create a small knowledge base by ablating a large KB, and simulate learning by incrementally re-adding facts, using different strategies to simulate types of learners. For each iteration, reasoning properties (including number of questions answered and run time) are collected, to explore how learning strategies and reasoning interact. We describe several experiments with the inverse ablation model, examining how two different learning strategies perform. Our results suggest that different concepts show different rates of growth, and that the density and distribution of facts that can be learned are important parameters for modulating the rate of learning.

Many signals of interest are corrupted by faults of anunknown type. We propose an approach that uses Gaus-sian processes and a general “fault bucket” to capturea priori uncharacterised faults, along with an approxi-mate method for marginalising the potential faultinessof all observations. This gives rise to an efficient, flexible algorithm for the detection and automatic correction of faults. Our method is deployed in the domain of water monitoring and management, where it is able to solve several fault detection, correction, and prediction problems. The method works well despite the fact that the data is plagued with numerous difficulties, including missing observations, multiple discontinuities, nonlinearity and many unanticipated types of fault.