Representing First-Order Causal Theories by Logic Programs

Nonmonotonic causal logic, introduced by Norman McCain and Hudson Turner, became a basis for the semantics of several expressive action languages. McCain's embedding of definite propositional causal theories into logic programming paved the way to the use of answer set solvers for answering queries about actions described in such languages. In this paper we extend this embedding to nondefinite theories and to first-order causal logic.Comment: 29 pages. To appear in Theory and Practice of Logic Programming (TPLP); Theory and Practice of Logic Programming, May, 201

Representing First-Order Causal Theories by Logic Programs

Nonmonotonic causal logic, introduced by McCain and Turner (McCain, N. and Turner, H. 1997. Causal theories of action and change. In Proceedings of National Conference on Artificial Intelligence (AAAI), Stanford, CA, 460–465) became the basis for the semantics of several expressive action languages. McCain\u27s embedding of definite propositional causal theories into logic programming paved the way to the use of answer set solvers for answering queries about actions described in such languages. In this paper we extend this embedding to nondefinite theories and to the first-order causal logic

Bringing action language C+ to normative contexts: preliminary report

C+ is an action language for specifying and reasoning about the e ects of actions and the persistence of facts over time. Based on it. we present CN+, an operational enhanced form of C+ designed for representing complex normative systems and integrate them easily into the semantics of the causal theory of actions. The proposed system contains a particular formalization of norms using a life-cycle approach to capture the whole normative meaning of a complex normative framework. We discuss this approach and illustrate it with examples.Peer ReviewedPostprint (author’s final draft

Naval Integration into Joint Data Strategies and Architectures in JADC2

NPS NRP Technical ReportAs Joint capabilities mature and shape into the Joint All Domain C2 Concept, Services, COCOMs and Coalition Partners will need to invest into efforts that would seamlessly integrate into Joint capabilities. The objective for the Navy is to study the options for Navy, including Naval Special Warfare Command under SOCOM, on how to integrate Navy's data strategy and architecture under the unifying JADC2 umbrella. The other objectives are to explore alternatives considered by the SOCOM and the Air Force, which are responsible for JADC2 Information Advantage and Digital Mission Command & Control. A major purpose of Joint, Services/COCOMs, agencies and Coalition Partners capabilities is to provide shared core of integrated canonical services for data, information, and knowledge with representations for vertical interoperability across all command levels and JADC2, lateral interoperability between Naval Service/COCOMs, and any combination of JADC2 constituents, agencies, and coalition partners. Our research plan is to explore available data strategy options by leveraging previous NRP work (NPS-20-N313-A). We will participate in emerging data strategy by Navy JADC2 project Overmatch. By working with MITRE our team will explore Air Force JADC2 data strategy implemented in ABMS DataOne component. Our goal is to find a seamless integration between Naval Data Strategy and data strategies behind JADC2 Information Advantage and Digital Mission Command & Control capabilities. Our plan includes studying Service-to-Service and Service-to-COCOM interoperability options required for Joint operations with a goal to minimize OODA's loop latency across sensing, situation discovery & monitoring, and knowledge understanding-for-planning, deciding, and acting. Our team realizes JADC2 requires virtual model allowing interoperability between subordinate C2 for services, agencies, and partner. Without such flexible 'joint' intersection organizational principal hierarchical structure it would be impossible to define necessary temporal and spatial fidelities for each level of organizational command required for implanting JADC2. Research deliverables will document the results of the exploration of Joint, COCOM, Agency and Partner Data Strategies approaches as JADC2 interoperability options to the emerging JADC2. We strive for standard JADC2 interface. Keywords: JADC2, ABMS, DataOne, Information Advantage, Digital Mission Command, IntegrationN2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

A review of the Handbook of Knowledge Representation Frankvan HarmelenVladimirLifschitzBrucePorterHandbook of Knowledge Representation2008Elsevier

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A review of proposed principles of causal non-monotonic reasoning

Within Non-monotonic Reasoning, numerous principles of causal reasoning have been proposed. Many of these principles have been viewed as desirable in formalisms that reason with causality, and have been widely adopted throughout the literature. We provide a critique of these principles, evaluate their suitability for characterising and formulating causal non-monotonic reasoning, and find that most are unsuitable. Further, we discuss a new approach to causal non-monotonic reasoning motivated by how humans typically reason with causality

Reasoning about Actions with Temporal Answer Sets

In this paper we combine Answer Set Programming (ASP) with Dynamic Linear Time Temporal Logic (DLTL) to define a temporal logic programming language for reasoning about complex actions and infinite computations. DLTL extends propositional temporal logic of linear time with regular programs of propositional dynamic logic, which are used for indexing temporal modalities. The action language allows general DLTL formulas to be included in domain descriptions to constrain the space of possible extensions. We introduce a notion of Temporal Answer Set for domain descriptions, based on the usual notion of Answer Set. Also, we provide a translation of domain descriptions into standard ASP and we use Bounded Model Checking techniques for the verification of DLTL constraints.Comment: To appear in Theory and Practice of Logic Programmin

Housekeeping with multiple autonomous robots: representation, reasoning, and execution

We consider a housekeeping domain with static or movable objects, where the goal is for multiple autonomous robots to tidy a house collaboratively in a given amount of time. This domain is challenging in the following ways: commonsense knowledge (e.g., expected locations of objects in the house) is required for intelligent behavior of robots; geometric constraints are required to find feasible plans (e.g., to avoid collisions); in case of plan failure while execution (e.g., due to a collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted by a single robot), recovery is required depending on the cause of failure; and collaboration of robots is required to complete some tasks (e.g., carrying heavy objects). We introduce a formal planning, execution and monitoring framework to address the challenges of this domain, by embedding knowledge representation and automated reasoning in each level of decision-making (that consists of discrete task planning, continuous motion planning, and plan execution), in such a way as to tightly integrate these levels. At the high-level, we represent not only actions and change but also commonsense knowledge in a logicbased formalism. Geometric reasoning is lifted to the high-level by embedding motion planning in the domain description. Then a discrete plan is computed for each robot using an automated reasoner. At the mid-level, if a continuous trajectory cannot be computed by a motion planner because the discrete plan is not feasible at the continuous-level, then a different plan is computed by the automated reasoner subject to some (temporal) conditions represented as formulas. At the low-level, if the plan execution fails, then a new continuous trajectory is computed by a motion planner at the mid-level or a new discrete plan is computed using an automated reasoner at the high-level. We illustrate the applicability of this formal framework with a simulation of a housekeeping domain