Presence Of Incomplete Knowledge Research Articles

Science, Medicine, and the Anesthesiologist

Science, Medicine, and the Anesthesiologist

Hybrid conditional planning for robotic applications

Robots who have partial observability of and incomplete knowledge about their environments may have to consider contingencies while planning, and thus necessitate cognitive abilities beyond classical planning. Moreover, during planning, they need to consider continuous feasibility checks for executability of the plans in the real world. Conditional planning is concerned with reaching goals from an initial state, in the presence of incomplete knowledge and partial observability, by considering all contingencies and by utilizing sensing actions to gather relevant knowledge when needed. A conditional plan is essentially a tree of actions where each branch of the tree represents a possible execution of actuation actions and sensing actions to reach a goal state. Hybrid conditional planning extends conditional planning by integrating feasibility checks into executability conditions of actions. We introduce a parallel offline algorithm, called HCPlan, for computing hybrid conditional plans. HCPlan relies on modeling deterministic effects of actuation actions and non-deterministic effects of sensing actions in the causality-based action language . Branches of a hybrid conditional plan are computed in parallel using a SAT solver, where continuous feasibility checks are performed as needed. We develop a comprehensive benchmark suite and introduce new evaluation metrics for hybrid conditional planning. We evaluate HCPlan with extensive experiments in terms of computational efficiency and plan quality. We perform experiments to compare HCPlan with other related conditional planners and approaches to deal with contingencies due to incomplete knowledge. We further demonstrate the applicability and usefulness of HCPlan in service robotics applications, through dynamic simulations and physical implementations.

Extending Monte Carlo Simulations to Represent and Propagate Uncertainties in Presence of Incomplete Knowledge: Application to the Transfer of a Radionuclide in the Environment

This work is devoted to some recent developments in uncertainty analysis of environmental models in the presence of incomplete knowledge. The classical uncertainty methodology based on probabilistic modeling provides direct estimations of relevant statistical measures to quantify the uncertainty on the model responses thanks to a nice mixing between Monte Carlo simulations and the use of efficient statistical treatments. However, this approach may lead to unrealistic results when not enough information is available to specify the probability distribution functions (pdfs) of input parameters. For example, if a fixed (i.e., the pdf is a Dirac distribution) variable is unknown between a and b , the proper way to model this knowledge is to consider a set of δc distributions (a δc distribution means that the probability that the parameter is equal to c is 1 and 0 elsewhere), c belonging to [a,b] . This is quite different from assume an equidistribution. Thus, to respect the real state of knowledge in industria...

Designing logic-based robots.

A rational agent exploring a complex and dynamic environment with incomplete information needs cognitive capabilities, e.g. planning, in addition to its perception and reaction for basic functionalities. However, mere planning, i.e., reasoning about sequences of actions, is not sufficient to solve problems in such complex environment. This is because (i) agents need to execute actions while they plan, (ii) they must gather and interpret sensor information, (iii) revise their world model, and (iv) adapt their own goals during a task. The knowledge representation and non monotonic reasoning area has shown the advantage of using logical formalisms to specify rational agents for complex robot applications, also called cognitive robotics applications. An example of such formalism is the Golog language and its different dialects. What those areas are still missing is implementational testbeds to evaluate existing theories. Since practical experimentation highlights the need for the improvement of existing formal theories at the ontological level. This work presents a logic-based implementation of an agent for the Wumpus World domain, which can be envisaged as a simplified model of an agent that reasons logically about its actions and sensor information in the presence of incomplete knowledge. Through a complete implementation of an agent in a real robot, the LegoMindStorm robot, we show some experiments to verify that the Wumpus World can be an implementational testbed used to identify difficulties in transforming theory into operational solutions.

FLUX: A logic programming method for reasoning agents

FLUX is a programming method for the design of agents that reason logically about their actions and sensor information in the presence of incomplete knowledge. The core of FLUX is a system of Constraint Handling Rules, which enables agents to maintain an internal model of their environment by which they control their own behavior. The general action representation formalism of the fluent calculus provides the formal semantics for the constraint solver. FLUX exhibits excellent computational behavior due to both a carefully restricted expressiveness and the inference paradigm of progression.

Integrating induction and abduction in logic programming

We propose an approach for the integration of abduction and induction in Logic Programming. We define an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main benefits of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning Abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four different learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates.