James A. Hendler

AIJ Journal 2016 Journal Article

Semantic sensitive tensor factorization

• Makoto Nakatsuji
• Hiroyuki Toda
• Hiroshi Sawada
• Jin Guang Zheng
• James A. Hendler

The ability to predict the activities of users is an important one for recommender systems and analyses of social media. User activities can be represented in terms of relationships involving three or more things (e. g. when a user tags items on a webpage or tweets about a location he or she visited). Such relationships can be represented as a tensor, and tensor factorization is becoming an increasingly important means for predicting users' possible activities. However, the prediction accuracy of factorization is poor for ambiguous and/or sparsely observed objects. Our solution, Semantic Sensitive Tensor Factorization (SSTF), incorporates the semantics expressed by an object vocabulary or taxonomy into the tensor factorization. SSTF first links objects to classes in the vocabulary (taxonomy) and resolves the ambiguities of objects that may have several meanings. Next, it lifts sparsely observed objects to their classes to create augmented tensors. Then, it factorizes the original tensor and augmented tensors simultaneously. Since it shares semantic knowledge during the factorization, it can resolve the sparsity problem. Furthermore, as a result of the natural use of semantic information in tensor factorization, SSTF can combine heterogeneous and unbalanced datasets from different Linked Open Data sources. We implemented SSTF in the Bayesian probabilistic tensor factorization framework. Experiments on publicly available large-scale datasets using vocabularies from linked open data and a taxonomy from WordNet show that SSTF has up to 12% higher accuracy in comparison with state-of-the-art tensor factorization methods.

IS Journal 2013 Journal Article

Open Government Data: A Data Analytics Approach

• John S. Erickson
• Amar Viswanathan
• Joshua Shinavier
• Yongmei Shi
• James A. Hendler

The International Open Government Dataset Search (IOGDS) team discusses what they've learned about international government data publication trends and tendencies through the application of data analytics and visualization to metadata.

TIST Journal 2012 Journal Article

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

• Xiaoqin Shelley Zhang
• Bhavesh Shrestha
• Sungwook Yoon
• Subbarao Kambhampati
• Phillip DiBona
• Jinhong K. Guo
• Daniel McFarlane
• Martin O. Hofmann

We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

AAAI Conference 1999 Conference Paper

Using Planning Graphs for Solving HTN Planning Problems

• Amnon Lotem
• Dana S. Nau
• James A. Hendler
• University of Maryland

In this paper we present the GraphHTN algorithm, a hybrid planning algorithm that does Hierarchical Task-Network (HTN) planning using a combination of HTN-style problem reduction and Graphplan-style planning-graph generation. We also present experimental results comparing GraphHTN with ordinary HTN decomposition (as implemented in the UMCP planner) and ordinary Graphplan search (as implemented in the IPP planner). Our experimental results show that (1) the performance of HTN planning can be improved significantly by using planning graphs, and (2) that planning with planning graphs can be sped up by exploiting HTN control knowledge.

ICRA Conference 1995 Conference Paper

A Motion Description Language and a Hybrid Architecture for Motion Planning. with Nonholonomic Robots

• Vikram Manikonda
• P. S. Krishnaprasad
• James A. Hendler

This paper puts forward a formal basis for behavior-based robotics, using techniques that have been successful in control-theory-based approaches for steering and stabilizing robots that are subject to nonholonomic constraints. In particular, behaviors for robots are formalized in terms of kinetic state machines, a motion description language, and the interaction of the kinetic state machine with real-time information from (limited range) sensors. This formalization allows us to create a mathematical basis for the study of such systems, including techniques for integrating sets of behaviors. In addition we suggest optimality criteria for comparing both atomic and compound behaviors in various environments. A hybrid architecture for the implementation of path planners that uses the motion description language is also presented.

ICAPS Conference 1994 Conference Paper

The Use of Supervenience in Dynamic-world Planning

• Lee Spector
• James A. Hendler

Thispaperdescribesthe use of supervenience in integrating planningand reaction in complex, dynamicenvironments. Supervenience is a formof abstractionwithaffinities both to abstraction in AI planningsystemsand to partitioning schemesin hierarchical control systems. Theuse of supervenieneecanbe distilled to an easy-to-stateconstrainton the designof multileveldynamic-world p "lanningsystems: worldknowledgeup, goals down. Wepresent the supervenience architecturewhichembodies this constraint, and contrast it to thc subsumption architecture of Brooks. Wedescribe the performanceof an implementationof the supervcnience architecture on a problemin the HomeBot domain, and we concludewitha discussionof the role that supcrvenience can play in future dynamic-world planningsystems. Supervenienceis a species of abstraction that wc believe to be important for systems that must integrate high-level reasoning with real-time action. Simplification abstraction is a special case of supervenience, and the search-reduction benefits of ABSTRIPS-stylesystems are sometimesavailable in supcrvenient planning systems as well. The generality of supervenience also allows, however, for uses of abstraction similar to those available in blackboard architectures and in other multilevel control systems. The central idea of supervenienceis that representations at lower levels of abstraction are epistemologically"closer to the world"than those at higher levels, and that the representations at higher levels therefore dependon those at lower levels. The higher levels maycontain representations that axe simplifications of low-level, sensory reports, but they mayjust as well contain representations that axe complex, structurally rich aggregates that have no unified representation at lower levels. In contrast to ABSTRIPSstyle systems, in which higher levels must be simplifications of the lower levels, levels of supervenience maybe dissimilar in various ways so long as the proper dependence relation holds. The thesis is that it is this dependence, and not the more restrictive notion of simplification, that allows for the flexible integration of deliberation and reaction. The concept of supervenienccapplies naturally to multilevel computationalarchitectures in which the higher levels are coupled to the world through the lower levels. In such cases the privileged status of the lower levels (vis-avis access to the world) can be used to advantage. Wehave formalized the superveniencc relation in the context of nonmonotonic reasoning systems, using the concept of "defeasibility" in nonmonotonicsystems to spell out the appropriate notion of "dependence. " Supervenienceis defined to be the case in whichlower levels can defeat higher level facts but not vice versa (Specter 1992); this can abbreviated as "assertions up, assumptions down, "and reformulated for implementation purposes as "world knowledge up, goals down. "The bottom line for system-builders is this: Lowlevels should "know" enough to be right about, and to act upon, their assertions. Highlevels should configure (e. g., provide goals for) lowerlevels, but should not override knowledgedetermined to be true by the lower levels. Lowerlevels mayneed to monitor for goal-changes from above, but not for changes in world knowledgefrom

ICAPS Conference 1994 Conference Paper

UMCP: A Sound and Complete Procedure for Hierarchical Task-network Planning

• Kutluhan Erol
• James A. Hendler
• Dana S. Nau

One big obstacle to understanding the nature of hierarchical task network(HTN)planning has been the lack of a dear theoretical framework. In particular, no one has yet presented a clear and concise HTNalgorithm that is sound and complete. In this paper, we present a formal syntax and semantics for HTNplanning. Based on this syntax and semantics, we are able to define an algo~thm for HTNplanning and prove it sound m~dcomplete.

AIJ Journal 1992 Journal Article

A validation-structure-based theory of plan modification and reuse

• Subbarao Kambhampati
• James A. Hendler

The ability to modify existing plans to accommodate a variety of externally imposed constraints (such as changes in the problem specification, the expected world state, or the structure of the plan) is a valuable tool for improving efficiency of planning by avoiding repetition of planning effort. In this paper, we present a theory of incremental plan modification suitable for hierarchical nonlinear planning, and describe its implementation in a system called PRIAR. In this theory, the causal and teleological structure of the plans generated by a planner are represented in the form of an explanation of correctness called the “validation structure”. Individual planning decisions are justified in terms of their relation to the validation structure. Plan modification is formalized as a process of removing inconsistencies in the validation structure of a plan when it is being reused in a new or changed planning situation. The repair of these inconsistencies involves removing unnecessary parts of the plan and adding new nonprimitive tasks to the plan to establish missing or failing validations. The result is a partially reduced plan with a consistent validation structure, which is then sent to the planner for complete reduction. We discuss this theory, present an empirical evaluation of the resulting plan modification system, and characterize the coverage, efficiency and limitations of the approach.

IJCAI Conference 1989 Conference Paper

Control of Refitting during Plan Reuse

• Subbarao Kambhampati
• James A. Hendler

IJCAI Conference 1987 Conference Paper

Marker-Passing and Microfeatures

• James A. Hendler