Chitta Baral
Arizona State University, Computer Science, Faculty Member
Abstract In this paper we show that despite a past claim goals such as 'try your best to make p true'in presence of non-deterministic actions can be expressed in the framework of branching time temporal logic. We analyze the A and E... more
Abstract In this paper we show that despite a past claim goals such as 'try your best to make p true'in presence of non-deterministic actions can be expressed in the framework of branching time temporal logic. We analyze the A and E operators in CTL∗ and point out why it was thought that the above mentioned goal can not be expressed using CTL∗.
Abstract In this paper we discuss the applicability of the knowledge representation and reasoning language AnsProlog (Answer Set Programming) for the design and implementation of a query answering system (QAS) for homeland security. We... more
Abstract In this paper we discuss the applicability of the knowledge representation and reasoning language AnsProlog (Answer Set Programming) for the design and implementation of a query answering system (QAS) for homeland security. We discuss our work to date on using AnsProlog to axiomatize the travel domain. We illustrate how it can be used to represent defaults, causal relations, and other types of commonsense knowledge needed to properly answer non-trivial questions about this domain.
Recent research in diagnosis (Mcllraith, 1997; Thielscher, 1997; McIlraith, 1998) extends earlier works in diagnosis fi'om first principles by using an action theory instead of a first-order theory to describe the correct behavior of the... more
Recent research in diagnosis (Mcllraith, 1997; Thielscher, 1997; McIlraith, 1998) extends earlier works in diagnosis fi'om first principles by using an action theory instead of a first-order theory to describe the correct behavior of the system in consideration. The action theory allows us to reason about actions and their effects. Thus, when an action does not yield the expected effects something did mMfunction. Using regression, we can identify what is wrong initially. Our position is that in many cases this is not enough.
Curated biological knowledge of interactions and pathways is largely available from various databases, and network synthesis is a popular method to gain insight into the data. However, such data from curated databases presents a single... more
Curated biological knowledge of interactions and pathways is largely available from various databases, and network synthesis is a popular method to gain insight into the data. However, such data from curated databases presents a single view of the knowledge to the biologists, and it may not be suitable to researchers‟ specific needs. On the other hand, Medline abstracts are publicly accessible and encode the necessary information to synthesize different kinds of biological networks.
We develop a high level action description language to express knowledge about cellular processes and mechanisms. This involves representation and reasoning about both discrete properties and continuous processes. Both of them may be... more
We develop a high level action description language to express knowledge about cellular processes and mechanisms. This involves representation and reasoning about both discrete properties and continuous processes. Both of them may be changed by exogenous actions or triggers. We use differential equations to represent continuous processes. We give syntax and semantics of the language and also present an approximate characterization. We present a temporal query language for such domains.
Abstract Expert knowledge consists of statements Sj (facts and rules). The expert's degree of con dence in each statement Sj can be described as a (subjective) probability (some probabilities are known to be independent). Examples: if we... more
Abstract Expert knowledge consists of statements Sj (facts and rules). The expert's degree of con dence in each statement Sj can be described as a (subjective) probability (some probabilities are known to be independent). Examples: if we are interested in oil, we should look at seismic data (con dence 90%); a bank A trusts a client B, so if we trust A, we should trust B too (con dence 99%). If a query Q is deducible from facts and rules, what is our con dence p (Q) in Q?
Motivation: The promises of the post-genome era disease-related discoveries and advances have yet to be fully realized, with many opportunities for discovery hiding in the millions of biomedical papers published since. Public databases... more
Motivation: The promises of the post-genome era disease-related discoveries and advances have yet to be fully realized, with many opportunities for discovery hiding in the millions of biomedical papers published since. Public databases give access to data extracted from the literature by teams of experts, but their coverage is often limited and lags behind recent discoveries.
Abstract In this paper our goal is to bridge two popular and wellstudied knowledge representation formalisms: description logics (DLs), and declarative logic programs (DLPs). In recent years there has been tremendous development in both... more
Abstract In this paper our goal is to bridge two popular and wellstudied knowledge representation formalisms: description logics (DLs), and declarative logic programs (DLPs). In recent years there has been tremendous development in both fields in terms of theoretical studies and implementations.
Abstract Pearl's probabilistic causal model has been used in many domains to reason about causality. Pearl's treatment of actions is very different from the way actions are represented explicitly in action languages. In this paper we show... more
Abstract Pearl's probabilistic causal model has been used in many domains to reason about causality. Pearl's treatment of actions is very different from the way actions are represented explicitly in action languages. In this paper we show how to encode Pearl's probabilistic causal model in the action language PAL thus relating this two distinct approaches to reasoning about actions.
Abstract Sensing actions are important for planning with incomplete information. A solution for the frame problem for sensing actions was proposed by Scherl and Levesque. They adapt the possible world model of knowledge to situation... more
Abstract Sensing actions are important for planning with incomplete information. A solution for the frame problem for sensing actions was proposed by Scherl and Levesque. They adapt the possible world model of knowledge to situation calculus. In this paper we propose a high level language in the spirit of the language A, that allows sensing actions. We then present two approximation semantics of this language and their translation to logic programs.
Abstract 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... more
Abstract 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).
Abstract This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for... more
Abstract This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node.
Gelfond and Lifschitz introduce a declarative languageAfor describing effects of actions and describe translations of theories in this language into extended logic programs. In this paper we extend the languageAand its translation to... more
Gelfond and Lifschitz introduce a declarative languageAfor describing effects of actions and describe translations of theories in this language into extended logic programs. In this paper we extend the languageAand its translation to allow reasoning about the effects of concurrent actions.
In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agent's knowledge about the world. In such a case the agent may need to have at its disposal sensing actions... more
In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agent's 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.
The Gelfond-Lifschitz operator associated with a logic program (and likewise the operator associated with default theories by Reiter) exhibits oscillating behavior. In the case of logic programs, there is always at least one finite,... more
The Gelfond-Lifschitz operator associated with a logic program (and likewise the operator associated with default theories by Reiter) exhibits oscillating behavior. In the case of logic programs, there is always at least one finite, nonempty collection of Herbrand interpretations around which the Gelfond-Lifschitz operator 'bounces around'. The same phenomenon occurs with default logic when Reiter's operator?? is considered. Based on this, a 'stable class' semantics and 'extension class' semantics has been proposed.
In the last several years, there have been several studies about the computational complexity of classical planning assuming that the planner has complete knowledge about the initial situation. Recently, there have been proposals to... more
In the last several years, there have been several studies about the computational complexity of classical planning assuming that the planner has complete knowledge about the initial situation. Recently, there have been proposals to usesensing'actions to plan in the presence of incompleteness. In this paper we study the complexity of planning in such cases. In our study we use the action description language A proposed in 1991 by Gelfond and Lifschitz, and its extensions.
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... more
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.
Abstract In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl. The main feature of our language is its use of static and dynamic causal laws, and use of... more
Abstract In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl. The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables–whose values are determined by factors beyond our model–in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial.
In this paper we take a first step towards characterizing active databases. Declarative characterization of active databases allows additional flexibility in studying the effects of different priority criteria between fireable rules,... more
In this paper we take a first step towards characterizing active databases. Declarative characterization of active databases allows additional flexibility in studying the effects of different priority criteria between fireable rules, different actions and event definitions, and also to make claims about effects of transaction and prove them without actually executing them.
Abstract Given a system and unexpected observations about the system, a diagnosis is often viewed as a fault assignment to the various components of the system that is consistent with (or that explains) the observations. If the... more
Abstract Given a system and unexpected observations about the system, a diagnosis is often viewed as a fault assignment to the various components of the system that is consistent with (or that explains) the observations. If the observations occur over time, and if we allow the occurrence of (deliberate) actions and (exogenous) events, then the traditional notion of a candidate diagnosis must be modified to consider the possible occurrence of actions and events that could account for the unexpected system behavior.
Abstract In this paper, we present a fully automated extraction system, named IntEx, to identify gene and protein interactions in biomedical text. Our approach is based on first splitting complex sentences into simple clausal structures... more
Abstract In this paper, we present a fully automated extraction system, named IntEx, to identify gene and protein interactions in biomedical text. Our approach is based on first splitting complex sentences into simple clausal structures made up of syntactic roles. Then, tagging biological entities with the help of biomedical and linguistic ontologies. Finally, extracting complete interactions by analyzing the matching contents of syntactic roles and their linguistically significant combinations.
Abstract The logics of knowledge are modal logics that have been shown to be effective in representing and reasoning about knowledge in multi-agent domains. Relatively few computational frameworks for dealing with computation of models... more
Abstract The logics of knowledge are modal logics that have been shown to be effective in representing and reasoning about knowledge in multi-agent domains. Relatively few computational frameworks for dealing with computation of models and useful transformations in logics of knowledge (eg, to support multi-agent planning with knowledge actions and degrees of visibility) have been proposed.
Abstract Gelfond and Lifschitz introduce a declarative language A for describing effects of actions and define a translation of theories in this language into extended logic programs (ELP, s). The purpose of this paper is to extend the... more
Abstract Gelfond and Lifschitz introduce a declarative language A for describing effects of actions and define a translation of theories in this language into extended logic programs (ELP, s). The purpose of this paper is to extend the language and the translation to allow reasoning about the effects of concurrent actions.
Abstract In this paper we give a formal characterization of reactive control using action theories. In the process we formalize the notion of a reactive control program being correct with respect to a given goal, a set of initial states,... more
Abstract In this paper we give a formal characterization of reactive control using action theories. In the process we formalize the notion of a reactive control program being correct with respect to a given goal, a set of initial states, and action theories about the agent/robot and about the exogenous actions. We give sufciency conditions that guarantee correctness and use it to give an automatic method for constructing provenly correct control modules.
Abstract We extend the 0-approximation of sensing actions and incomplete information in Son and Baral (2001) to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show... more
Abstract We extend the 0-approximation of sensing actions and incomplete information in Son and Baral (2001) to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show that the conditional planning problem with respect to this approximation is NP-complete.
In recent years, a few researchers have challenged past dogma and suggested methods (such as the IC algorithm) for inferring causal relationship among variables using steady state observations. In this paper, we present a modified IC... more
In recent years, a few researchers have challenged past dogma and suggested methods (such as the IC algorithm) for inferring causal relationship among variables using steady state observations. In this paper, we present a modified IC (mIC) algorithm that uses entropy to test conditional independence and combines the steady state data with partial prior knowledge of topological ordering in gene regulatory network, for jointly learning the causal relationship among genes. We evaluate our mIC algorithm using the simulated data.
Consider the construction of an expert system by encoding the knowledge of different experts. Suppose the knowledge provided by each expert is encoded into a knowledge base. Then the process of combining the knowledge of these different... more
Consider the construction of an expert system by encoding the knowledge of different experts. Suppose the knowledge provided by each expert is encoded into a knowledge base. Then the process of combining the knowledge of these different experts is an important and nontrivial problem. We study this problem here when the expert systems are considered to be first-order theories. We present techniques for resolving inconsistencies in such knowledge bases.
Abstract 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... more
Abstract 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.
Abstract The goal of most agents is not just to reach a goal state, but rather also (or alternatively) to put restrictions on its trajectory, in terms of states it must avoid and goals that it must 'maintain'. This is analogous to the... more
Abstract The goal of most agents is not just to reach a goal state, but rather also (or alternatively) to put restrictions on its trajectory, in terms of states it must avoid and goals that it must 'maintain'. This is analogous to the notions of 'safety'and 'stability'in the discrete event systems and temporal logic community.
Abstract Motivation: In this paper we propose to use recent developments in knowledge representation languages and reasoning methodologies for representing and reasoning about signaling networks. Our approach is different from most other... more
Abstract Motivation: In this paper we propose to use recent developments in knowledge representation languages and reasoning methodologies for representing and reasoning about signaling networks. Our approach is different from most other qualitative systems biology approaches in that it is based on reasoning (or inferencing) rather than simulation.
Abstract: We present a system to translate natural language sentences to formulas in a formal or a knowledge representation language. Our system uses two inverse lambda-calculus operators and using them can take as input the semantic... more
Abstract: We present a system to translate natural language sentences to formulas in a formal or a knowledge representation language. Our system uses two inverse lambda-calculus operators and using them can take as input the semantic representation of some words, phrases and sentences and from that derive the semantic representation of other words and phrases. Our inverse lambda operator works on many formal languages including first order logic, database query languages and answer set programming.
In a combinatorial auction problem bidders are allowed to bid on a bundle of items. The auctioneer has to select a subset of the bids so as to maximize the price it gets, and of course making sure that it does not accept multiple bids... more
In a combinatorial auction problem bidders are allowed to bid on a bundle of items. The auctioneer has to select a subset of the bids so as to maximize the price it gets, and of course making sure that it does not accept multiple bids that have the same item as each item can be sold only once. In this paper we show how the combinatorial auction problem and many of its extensions can be expressed in logic programming based systems such as Smodels and dlv.
In this paper we present a declarative approach to adding domain-dependent control knowledge for Answer Set Planning (ASP). Our approach allows different types of domain-dependent control knowledge such as hierarchical, temporal, or... more
In this paper we present a declarative approach to adding domain-dependent control knowledge for Answer Set Planning (ASP). Our approach allows different types of domain-dependent control knowledge such as hierarchical, temporal, or procedural knowledge to be represented and exploited in parallel, thus combining the ideas of control knowledge in HTN-planning, GOLOG-programming, and planning with temporal knowledge into ASP. To do so, we view domain-dependent control knowledge as sets of independent constraints.
Abstract The complexity of sentences characteristic to biomedical articles poses a challenge to natural language parsers, which are typically trained on large-scale corpora of non-technical text. We propose a text simplification process,... more
Abstract The complexity of sentences characteristic to biomedical articles poses a challenge to natural language parsers, which are typically trained on large-scale corpora of non-technical text. We propose a text simplification process, bioSimplify, that seeks to reduce the complexity of sentences in biomedical abstracts in order to improve the performance of syntactic parsers on the processed sentences. Syntactic parsing is typically one of the first steps in a text mining pipeline.
Abstract One of the main ways to specify goals of agents is to use temporal logics. Most existing temporal logics are monotonic. However, in representing goals of agents, we often require that goals be changed non-monotonically. For... more
Abstract One of the main ways to specify goals of agents is to use temporal logics. Most existing temporal logics are monotonic. However, in representing goals of agents, we often require that goals be changed non-monotonically. For example, the initial goal of the agent may be to be always in states where p is true. The agent may later realize that under certain conditions (exceptions) it is ok to be in states where p is not true.
Knowledge management and knowledge-based intelligence are areas of importance in today's economy and society, and their exploitation requires representation via the development of a declarative interface whose input language is based on... more
Knowledge management and knowledge-based intelligence are areas of importance in today's economy and society, and their exploitation requires representation via the development of a declarative interface whose input language is based on logic. Chitta Baral demonstrates how to write programs that behave intelligently by giving them the ability to express knowledge and reason about it. He presents a language, AnsProlog, for both knowledge representation and reasoning, and declarative problem solving.
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... more
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.
In this paper we extend the high level execution language ConGolog (developed at the University of Toronto) by adding to it a new construct which we call the htn-construct. The new construct improves ConGolog by allowing easy... more
In this paper we extend the high level execution language ConGolog (developed at the University of Toronto) by adding to it a new construct which we call the htn-construct. The new construct improves ConGolog by allowing easy specification of non-determinism when a partial ordering between a set of actions needs to be maintained. Furthermore, it allows temporal constraints to be specified easily.
Abstract 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... more
Abstract 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.
Abstract In this article we consider three different kinds of domain-dependent control knowledge (temporal, procedural and HTN-based) that are useful in planning. Our approach is declarative and relies on the language of logic programming... more
Abstract In this article we consider three different kinds of domain-dependent control knowledge (temporal, procedural and HTN-based) that are useful in planning. Our approach is declarative and relies on the language of logic programming with answer set semantics (AnsProlog*). AnsProlog* is designed to plan without control knowledge.
Abstract: We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains... more
Abstract: We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression.
If we want to find the shortest plan, then usually, we try plans of length 1, 2,…, until we find the first length for which such a plan exists. When the planning problem is difficult and the shortest plan is of a reasonable length, this... more
If we want to find the shortest plan, then usually, we try plans of length 1, 2,…, until we find the first length for which such a plan exists. When the planning problem is difficult and the shortest plan is of a reasonable length, this linear search can take a long time; to speed up the process, it has been proposed to use binary search instead.
Abstract The main problem of planning is to find a sequence of actions that an agent must perform to achieve a given objective. An important part of planning is checking whether a given plan achieves the desired objective. Historically,... more
Abstract The main problem of planning is to find a sequence of actions that an agent must perform to achieve a given objective. An important part of planning is checking whether a given plan achieves the desired objective. Historically, in AI, the planning and plan checking problems were mainly formulated and solved in a deterministic environment, when the initial state is known precisely and when the results of each action in each state is known (and uniquely determined).
The main problem of planning is to find a sequence ofactions that an agent must perform to achieve a givenobjective. An important part of planning is checkingwhether a given plan achieves the desired objective. Historically, in AI, the... more
The main problem of planning is to find a sequence ofactions that an agent must perform to achieve a givenobjective. An important part of planning is checkingwhether a given plan achieves the desired objective. Historically, in AI, the planning and plan checkingproblems were mainly formulated and solved in a deterministicenvironment, when the initial state is knownprecisely and when the results of each action in eachstate is known (and uniquely determined).
The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. These revision or extension are first formulated as theoretical hypotheses, then verified... more
The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. These revision or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge.
Abstract. Currently, most knowledge representation using logic programming with answer set semantics (AnsProlog) is 'flat'. In this paper we elaborate on our thoughts about a modular structure for knowledge representation and declarative... more
Abstract. Currently, most knowledge representation using logic programming with answer set semantics (AnsProlog) is 'flat'. In this paper we elaborate on our thoughts about a modular structure for knowledge representation and declarative problem solving formalism using AnsProlog. We present language constructs that allow defining of modules and calling of such modules from programs.
Abstract The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Computer Science Department, was pleased to present its 2006 Spring Symposium Series held March 27-29, 2006, at Stanford... more
Abstract The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Computer Science Department, was pleased to present its 2006 Spring Symposium Series held March 27-29, 2006, at Stanford University, California.