Golog and ConGolog are languages defined in the situation calculus for cognitive robotics. Given a Golog program \delta, its semantics is defined by a macro Do(\delta,s,s') that expands to a logical sentence that captures the conditions under which performing \delta in s can terminate in s'. A similarmacro is defined for ConGolog programs. In general, the logical sentences that these macros expand to are second-order, and in the case of ConGolog, may involve quantification over programs. In this paper, we show that by making use of the foundational axioms in the situation calculus, in particular, the second-order closure axiom about the space of situations, these macro expressions can actually be defined using first-order sentences.

In this paper we investigate three different approaches to encoding domain-dependent control knowledge for Answer-Set Planning. Starting with a standard imple- mentation of the action description language B, we add control knowledge expressed in the GOLOG logic pro- gramming language. A naive encoding, following the original definitions of Levesque et al., is shown to scale poorly. We examine two alternative codings based on the transition semantics of ConGOLOG. We show that a speed increase of multiple orders of magnitude can be obtain by compiling the GOLOG program into a finite- state machine representation.

Much of the existing work on plan recognition assumes that actions of other agents can be observed directly. In continuous temporal domains such as traffic scenarios this assumption is typically not warranted. Instead, one is only able to observe facts about the world such as vehicle positions at different points in time, from which the agents' intentions need to be inferred. In this paper we show how this problem can be addressed in the situation calculus and a new variant of the action programming language Golog, which includes features such as continuous time and change, stochastic actions, nondeterminism, and concurrency. In our approach we match observations against a set of candidate plans in the form of Golog programs. We turn the observations into actions which are then executed concurrently with the given programs. Using decision-theoretic optimization techniques those programs are preferred which bring about the observations at the appropriate times. Besides defining this new variant of Golog we also discuss an implementation and experimental results using driving maneuvers as an example.

A wide range of problems, from contingent and multiagent planning to process/service orchestration, can be viewed as games. In many of these, it is natural to spec- ify the possible behaviors procedurally. In this paper, we develop a logical framework for specifying these types of problems/games based on the situation calculus and ConGolog. The framework incorporates game-theoretic path quantifiers as in ATL. We show that the framework can be used to model such problems in a natural way. We also show how verification/synthesis techniques can be used to solve problems expressed in the framework. In particular, we develop a method for dealing with infinite state settings using fixpoint approximation and “characteristic graphs”.

Despite the extensive development of first-principles planning in recent years, planning applications are still primarily developed using knowledge-based planners which can exploit domain-specific heuristics and weaker domain models.  Hierarchical Task Network (HTN) planners capture domain-specific heuristics for more efficient search, accommodate incomplete causal models, and can be used to enforce standard operating procedures.  Unfortunately, we do not have semantics for the methods or tasks that make up HTN models, that help evaluate the correctness of methods, or to build a reliable executive for HTN plans.  This paper fills the gap by providing a well-defined semantics for the methods and plans of SHOP2, a state-of-the-art HTN planner.  The semantics are defined in terms of concurrent golog (ConGolog) and the situation calculus.  We provide a proof of equivalence between the plans generated by SHOP2 and the action sequences of the ConGolog semantics.  We show how the semantics reflects the distinction between plan-time and execution-time, and provide some simple examples showing how the semantics can support method verification.  The semantics provide an implementation-neutral specification for an executive, showing how an executive must treat the plans SHOP2 generates in order to enforce the expected behaviors.  Future directions include automated verification of method specifications, automatically generating plan monitors, and plan revision and repair.

We look at composition of (possibly nonterminating) high-level programs over situation calculus action theories. Specifically the problem we look at is as follows: given a library of available ConGolog programs and a target program not in the library, verify whether the target program executions be realized by composing fragments of the executions of the available programs; and, if so, synthesize a controller that does the composition automatically. This kind of composition problems have been investigated in the CS and AI literature, but always assuming finite states settings. Here, instead, we investigate the issue in the context of infinite domains that may go through an infinite number of states as a result of actions.  Obviously in this context the problem is undecidable. Nonetheless, by exploiting recent results in the AI literature, we devise a sound and well characterized technique to actually solve the problem.