Refactoring the Whitby Intelligent Tutoring System for Clean Architecture

Whitby is the server-side of an Intelligent Tutoring System application for learning System-Theoretic Process Analysis (STPA), a methodology used to ensure the safety of anything that can be represented with a systems model. The underlying logic driving the reasoning behind Whitby is Situation Calculus, which is a many-sorted logic with situation, action, and object sorts. The Situation Calculus is applied to Ontology Authoring and Contingent Scaffolding: the primary activities within Whitby. Thus many fluents and actions are aggregated in Whitby from these two sub-applications and from Whitby itself, but all are available through a common situation query interface that does not depend upon any of the fluents or actions. Each STPA project in Whitby is a single situation term, which is queried for fluents that include the ontology, and to determine what pedagogical interventions to offer. Initially Whitby was written in Prolog using a module system. In the interest of a cleaner architecture and implementation with improved code reuse and extensibility, the initial application was refactored into Logtalk. This refactoring includes decoupling the Situation Calculus reasoner, Ontology Authoring framework, and Contingent Scaffolding framework into third-party libraries that can be reused in other applications. This extraction was achieved by inverting dependencies via Logtalk protocols and categories, which are reusable interfaces and components that provide functionally cohesive sets of predicate declarations and predicate definitions. In this paper the architectures of two iterations of Whitby are evaluated with respect to the motivations behind the refactor: clean architecture enabling code reuse and extensibility.

The Nondeterministic Situation Calculus

The standard situation calculus assumes that atomic actions are deterministic. But many domains involve nondeterministic actions, with problems such as fully observable nondeterministic (FOND) planning and high-level program execution requiring solutions. Various approaches have been proposed to accommodate nondeterminism on top of the standard situation calculus language, for instance by introducing nondeterministic programs as in Golog and ConGolog. But a key problem in these approaches is that they don’t clearly distinguish between choices that can be made by the agent and choices that are made by the environment, i.e., angelic vs. devilish nondeterminism. In this paper, we propose a simple extension to the standard situation calculus that accommodates nondeterministic actions and preserves Reiter’s solution to the frame problem and answering projection queries through regression. We also provide a formalization of FOND planning and show how ConGolog high-level program execution in nondeterministic domains can be defined.

An Account of Intensional and Extensional Actions, and its Application to Belief, Nondeterministic Actions and Fallible Sensors

In general, an agent may have incomplete and inaccurate knowledge about its environment. As well, actions may not turn out as intended or may have nondeterministic effects, and sensors may on occasion give incorrect results. We present a general, qualitative approach to reasoning about action and change in such a setting. The approach is expressed as an extension to basic action theories in the situation calculus, where an agent's epistemic state is modelled by a set of situations, where each situation is assigned a non-negative integer representing its plausibility. The agent's epistemic state is updated by modifying these plausibility values after the execution of an action, taking into account the possibility of unexpected results. To this end, we consider actions to have an intensional aspect, under the control of and determined by the agent, and an extensional aspect, not directly accessible to the agent and controlled by "nature". This leads to two distinct but related related notions of belief, an extensional "bird's eye" view which models an agent's beliefs wrt actually-executed actions, and an intensional view representing beliefs from the agent's point of view. We argue that the approach is significantly more general and comprehensive than previous accounts, and leads to a unified view of failed actions and nondeterminism with respect to physical and sensing actions.

On the Progression of Belief

Based on weighted possible-world semantics, Belle and Lakemeyer recently proposed the logic DS, a probabilistic extension of a modal variant of the situation calculus with a model of belief. The logic has many desirable properties like full introspection and it is able to precisely capture the beliefs of a probabilistic knowledge base in terms of the notion of only-believing. While the proposal is intuitively appealing, it is unclear how to do planning with such logic. The reason behind this is that the logic lacks projection reasoning mechanisms. Projection reasoning, in general, is to decide what holds after actions. Two main solutions to projection exist: regression and progression. Roughly, regression reduces a query about the future to a query about the initial state while progression, on the other hand, changes the initial state according to the effects of actions and then checks whether the formula holds in the updated state. In this paper, we study projection by progression in the logic DS. It is known that the progression of a categorical knowledge base wrt a noise-free action corresponds to what is only-known after that action. We show how to progress a type of probabilistic knowledge base wrt noisy actions by the notion of only-believing after actions. Our notion of only-believing is closely related to Lin and Reiter's notion of progression.

An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts

We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.

A Uniform Abstraction Framework for Generalized Planning

Generalized planning aims at finding a general solution for a set of similar planning problems. Abstractions are widely used to solve such problems. However, the connections among these abstraction works remain vague. Thus, to facilitate a deep understanding and further exploration of abstraction approaches for generalized planning, it is important to develop a uniform abstraction framework for generalized planning. Recently, Banihashemi et al. proposed an agent abstraction framework based on the situation calculus. However, expressiveness of such an abstraction framework is limited. In this paper, by extending their abstraction framework, we propose a uniform abstraction framework for generalized planning. We formalize a generalized planning problem as a triple of a basic action theory, a trajectory constraint, and a goal. Then we define the concepts of sound abstractions of a generalized planning problem. We show that solutions to a generalized planning problem are nicely related to those of its sound abstractions. We also define and analyze the dual notion of complete abstractions. Finally, we review some important abstraction works for generalized planning and show that they can be formalized in our framework.

Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic

In a recent paper Belle and Lakemeyer proposed the logic DS, a probabilistic extension of a modal variant of the situation calculus with a model of belief based on weighted possible worlds. Among other things, they were able to precisely capture the beliefs of a probabilistic knowledge base in terms of the concept of only-believing. While intuitively appealing, the logic has a number of shortcomings. Perhaps the most severe is the limited expressiveness in that degrees of belief are restricted to constant rational numbers, which makes it impossible to express arbitrary belief distributions. In this paper we will address this and other shortcomings by extending the language and modifying the semantics of belief and only-believing. Among other things, we will show that belief retains many but not all of the properties of DS. Moreover, it turns out that only-believing arbitrary sentences, including those mentioning belief, is uniquely satisfiable in our logic. For an interesting class of knowledge bases we also show how reasoning about beliefs and meta-beliefs after performing noisy actions and sensing can be reduced to reasoning about the initial beliefs of an agent using a form of regression.

Plan recognition in dynamic 2-D environments

We look at the relatively unexplored problem of plan recognition applied to motion in 2-D environments where all moving objects are modelled as circles. Golog is a well-known high level logical language for solving planning problems and specifying agent controllers. Few studies have applied Golog to plan recognition. We use some of the features of this language, but its standard interpreter is adapted to solving plan recognition problems. This thesis makes several other contributions. First, plan recognition procedures are formulated as finite automata and expressed as Golog programs. Second, we elaborate a logical formalism for reasoning about depth and motion from an observer's viewpoint. We not only expand on this situation calculus based formalism, but also apply it to tackle plan recognition problems in the traffic domain. The proposed approach is implemented and thoroughly tested on recognizing simple behaviours such as left turns, right turns, and overtaking.