cc-Golog: Towards More Realistic Logic-Based Robot Controllers

High-level robot controllers in realistic domains typically deal with processes which operate concurrently, change the world continuously, and where the execution of actions is event-driven as in ``charge the batteries as soon as the voltage level is low''. While non-logic-based robot control languages are well suited to express such scenarios, they fare poorly when it comes to projecting, in a conspicuous way, how the world evolves when actions are executed. On the other hand, a logic-based control language like \congolog, based on the situation calculus, is well-suited for the latter. However, it has problems expressing event-driven behavior. In this paper, we show how these problems can be overcome by first extending the situation calculus to support continuous change and event-driven behavior and then presenting \ccgolog, a variant of \congolog which is based on the extended situation calculus. One benefit of \ccgolog is that it narrows the gap in expressiveness compared to non-logic-based control languages while preserving a semantically well-founded projection mechanism

Probabilistic, Temporal Projections in ConGolog

The ability of mobile robots to project plans, i.e. to predict what will happen when they execute a plan, is crucial to flexibly plan and react in uncertain environments. While classical formalizations of this problem make strong assumptions about the world and restrict robot plans to simple, partially ordered sets of actions, real world robot controllers make use of concurrency, priorities and use probabilistic models. In this paper we present an extension of the situation calculus, representing time, concurrency, probabilistic belief, probabilistic action effects and complex plans, integrating and extending several previous proposal into a single formal framework. We apply this framework to model and reason about the behavior of a mobile service robot

Class Relevant Pattern Mining in Output-Polynomial Time

The set of so-called relevant patterns is a subset of all itemsets particularly suited for pattern-based classification tasks. So far, no efficient algorithm has been developed for computing the set of relevant patterns: all existing solutions have a worst-case complexity which is exponential in the size of the input and output. In this paper, we investigate new properties of the relevant patterns and develop, thereupon, the first algorithm whose runtime is polynomial in the size of the input and output. As we show in the experimental section, this result is not only of theoretical interest but also of practical importance, often reducing the search space by orders of magnitude.

Towards more realistic logic based robot controllers in the GOLOG framework

High-level robot control languages should not only be expressive but should also support reasoning about actions, in particular, the projection of robot plans. Projection is useful for the robot when choosing among different courses of action as well as for the designer of robot controllers, since projections allow for qualitative simulations. The high-level programming language GOLOG was specifically proposed for this purpose. The semantics of GOLOG, which offers constructs such as sequences, iterations and recursive procedures, is based on the situation calculus, a logical language for reasoning about action and change. In particular, every primitive GOLOG action is an action of the underlying situation calculus theory, which allows reasoning about the effects of primitive actions or complex GOLOG programs. While GOLOG comes equipped with a powerful projection mechanism, however, it lacks the expressiveness provided by non-logic-based robot programming languages like RPL, RAP or Colbert. In particular, it does not provide facilities for dealing with continuous change, event-driven behavior, and communication with lower-level routines for navigation or localization, to which we refer to as low-level processes. Another limitation of GOLOG is that it assumes that actions have deterministic effects and cannot represent probabilistic uncertainty. In realistic domains, however, uncertainty seems to be ubiquitous: a robot has often only probabilistic beliefs about the state of the world, and low-level processes have probabilistic outcomes. In this thesis, we show how the GOLOG framework can be extended to deal with issues like continuous change, event-driven actions and low-level processes in a natural way, thus shortening the gap in expressiveness between non-logic-based and logic-based robot control languages. In particular, we integrate continuous time and change directly in the ontology of GOLOG. To facilitate the actual execution of high-level plans on a real robot, we employ a layered robot control architecture where a high-level controller communicates via message with the low-level processes provided by the basic-task execution system. Our framework allows not only the projection and the actual (on-line) execution of the same plans, but also supports the specification of plans with interleave projection and on-line execution. Furthermore, we provide means to represent and deal with probabilistic beliefs and noisy sensors and effectors. Finally, the extended GOLOG formalism is implemented in PROLOG and evaluated in several experiments, including delivery tasks where the mobile robot Carl operates in the Computer Science Department V at Aachen University of Technology

Towards more realistic logic based robot controllers in the GOLOG framework

High-level robot control languages should not only be expressive but should also support reasoning about actions, in particular, the projection of robot plans. Projection is useful for the robot when choosing among different courses of action as well as for the designer of robot controllers, since projections allow for qualitative simulations. The high-level programming language GOLOG was specifically proposed for this purpose. The semantics of GOLOG, which offers constructs such as sequences, iterations and recursive procedures, is based on the situation calculus, a logical language for reasoning about action and change. In particular, every primitive GOLOG action is an action of the underlying situation calculus theory, which allows reasoning about the effects of primitive actions or complex GOLOG programs. While GOLOG comes equipped with a powerful projection mechanism, however, it lacks the expressiveness provided by non-logic-based robot programming languages like RPL, RAP or Colbert. In particular, it does not provide facilities for dealing with continuous change, event-driven behavior, and communication with lower-level routines for navigation or localization, to which we refer to as low-level processes. Another limitation of GOLOG is that it assumes that actions have deterministic effects and cannot represent probabilistic uncertainty. In realistic domains, however, uncertainty seems to be ubiquitous: a robot has often only probabilistic beliefs about the state of the world, and low-level processes have probabilistic outcomes. In this thesis, we show how the GOLOG framework can be extended to deal with issues like continuous change, event-driven actions and low-level processes in a natural way, thus shortening the gap in expressiveness between non-logic-based and logic-based robot control languages. In particular, we integrate continuous time and change directly in the ontology of GOLOG. To facilitate the actual execution of high-level plans on a real robot, we employ a layered robot control architecture where a high-level controller communicates via message with the low-level processes provided by the basic-task execution system. Our framework allows not only the projection and the actual (on-line) execution of the same plans, but also supports the specification of plans with interleave projection and on-line execution. Furthermore, we provide means to represent and deal with probabilistic beliefs and noisy sensors and effectors. Finally, the extended GOLOG formalism is implemented in PROLOG and evaluated in several experiments, including delivery tasks where the mobile robot Carl operates in the Computer Science Department V at Aachen University of Technology

Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their nondeterministic effects, and several modes of their interferences

Towards more realistic logic-based robot controllers in the GOLOG framework

High-level robot control languages should not only be expressive enough for realistic domains but also support reasoning about actions, in particular, the projection of robot plans, which is useful for the robot when choosing among different courses of action as well as the designer of robot controllers, since projections allow for qualitative simulations. GOLOG, a language based on the situation calculus, was specifically proposed for this purpose. While it comes equipped with a powerful projection mechanism, however, it lacks expressiveness. In particular, it cannot deal with continuous change, event-driven behavior, and probabilistic effects of actions, all of which are important in the domain of mobile robotics. In this paper, we show how these issues can be dealt with in the GOLOG framework by proposing appropriate extensions of the language. Introduction In the last five years, substantial progress has been made in building mobile robots which can navigate safely ..

Causal Models of Mobile Service Robot Behavior

Temporal projection, the process of predicting what will happen when a robot executes its plan, is essential for autonomous service robots to successfully plan their missions. This paper describes a causal model of the behavior exhibited by the mobile robot RHINO when running concurrent reactive plans for performing office delivery jobs. The model represents aspects of robot behavior that cannot be represented by most action models used in AI planning: it represents the temporal structure of continuous control processes, several modes of their interferences, and various kinds of uncertainty. This enhanced expressiveness enables XFRM, a robot planning system, to predict, and therefore forestall, various kinds of behavior flaws including missed deadlines whilst exploiting incidental opportunities. The proposed causal model is experimentally validated using the robot and its simulator

Causal Models of Mobile Service Robot Behavior

Temporal projection, the process of predicting what will happen when a robot executes its plan, is essential for autonomous service robots to successfully plan their missions. This paper describes a causal model of the behavior exhibited by the mobile robot Rhino when running concurrent reactive plans for performing office delivery jobs. The model represents aspects of robot behavior that cannot be represented by most action models used in AI planning: it represents the temporal structure of continuous control processes, several modes of their interferences, and various kinds of uncertainty. This enhanced expressiveness enables xfrm (McD92; BM94), a robot planning system, to predict, and therefore forestall, various kinds of behavior flaws including missed deadlines whilst exploiting incidental opportunities. The proposed causal model is experimentally validated using the robot and its simulator. Introduction Temporal projection, the process of predicting what will happen when a ro..