Chitta Baral

Logic programming and knowledge representation

Abstract In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider extensions of the language of definite logic programs by classical (strong) negation, disjunction, and some modal operators and show how each of the added features extends the representational power of the language. We also discuss extensions of logic programming allowing abductive reasoning, meta-reasoning and reasoning in open domains. We investigate the methodology of using these languages for representing various forms of nonmonotonic reasoning and for describing knowledge in specific domains. We also address recent work on properties of programs needed for successful applications of this methodology such as consistency, categoricity and complexity.

Combining multiple knowledge bases

Combining knowledge present in multiple knowledge base systems into a single knowledge base is discussed. A knowledge based system can be considered an extension of a deductive database in that it permits function symbols as part of the theory. Alternative knowledge bases that deal with the same subject matter are considered. The authors define the concept of combining knowledge present in a set of knowledge bases and present algorithms to maximally combine them so that the combination is consistent with respect to the integrity constraints associated with the knowledge bases. For this, the authors define the concept of maximality and prove that the algorithms presented combine the knowledge bases to generate a maximal theory. The authors also discuss the relationships between combining multiple knowledge bases and the view update problem. >

Reasoning agents in dynamic domains

The paper discusses an architecture for intelligent agents based on the use of A-Prolog- a language of logic programs under the answer set semantics. A-Prolog is used to represent the agents reasoning tasks. We outline how these tasks can be reduced to answering questions about properties of simple logic programs and demonstrate the methodology of constructing these programs.

Formalizing sensing actions—a transition function based approach

Abstract In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agents knowledge about the world. In such a case the agent may need to have at its disposal sensing actions that change its state of knowledge about the world and may need to construct more general plans consisting of sensing actions and conditional statements to achieve its goal. In this paper we first develop a high-level action description language that allows specification of sensing actions and their effects in its domain description and allows queries with conditional plans. We give provably correct translations of domain description in our language to axioms in first-order logic, and relate our formulation to several earlier formulations in the literature. We then analyze the state space of our formulation and develop several sound approximations that have much smaller state spaces. Finally we define regression of knowledge formulas over conditional plans.

Probabilistic Reasoning With Answer Sets

We give a logic programming based account of probability and describe a declarative language P-log capable of reasoning which combines both logical and probabilistic arguments. Several non-trivial examples illustrate the use of P-log for knowledge representation.

Probabilistic reasoning with answer sets

This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs.

Representing actions: Laws, observations and hypotheses

Abstract We propose a modificationL 1 of the action description languageA. The languageL 1 allows representation of hypothetical situations and hypothetical occurrence of actions (as inA) as well as representation of actual occurrences of actions and observations of the truth values of fluents in actual situations. The corresponding entailment relation formalizes various types of common-sense reasoning about actions and their effects not modeled by previous approaches. As an application of L1 we also present an architecture for intelligent agents capable of observing, planning and acting in a changing environment based on the entailment relation of L1 and use logic programming approximation of this entailment to implement a planning module for this architecture. We prove the soundness of our implementation and give a sufficient condition for its completeness.

What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution

Robots that can be given instructions in spoken language need to be able to parse a natural language utterance quickly, determine its meaning, generate a goal representation from it, check whether the new goal conflicts with existing goals, and if acceptable, produce an action sequence to achieve the new goal (ideally being sensitive to the existing goals). In this paper, we describe an integrated robotic architecture that can achieve the above steps by translating natural language instructions incrementally and simultaneously into formal logical goal description and action languages, which can be used both to reason about the achievability of a goal as well as to generate new action scripts to pursue the goal. We demonstrate the implementation of our approach on a robot taking spoken natural language instructions in an office environment.

Discovering drug–drug interactions

Motivation: Identifying drug–drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning. Results: Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions. Contact: luis.tari@roche.com

Fuzzy c-means clustering with prior biological knowledge

We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.