Ramification Problem Research Articles

A tool for addressing the ramification problem in spatial databases: A solution implemented in SQL

In this paper, we study the ramification problem in the setting of spatial databases. Standard solutions from the literature on reasoning about action are inadequate because they cannot capture integrity constraints in spatial data. In this paper, we provide a solution to the ramification problem based on situation calculus. We present a tool that connects the theoretical results to practical considerations, by producing the appropriate SQL commands in order to address the ramification problem in spatial databases.

Inductive situation calculus

Temporal reasoning has always been a major test case for knowledge representation formalisms. In this paper, we develop an inductive variant of the situation calculus in ID-logic, classical logic extended with inductive definitions. This logic has been proposed recently and is an extension of classical logic. It allows for a uniform representation of various forms of definitions, including monotone inductive definitions and non-monotone forms of inductive definitions such as iterated induction and induction over well-founded posets. We show that the role of such complex forms of definitions is not limited to mathematics but extends to commonsense knowledge representation. In the ID-logic axiomatization of the situation calculus, fluents and causality predicates are defined by simultaneous induction on the well-founded poset of situations. The inductive approach allows us to solve the ramification problem for the situation calculus in a uniform and modular way. Our solution is among the most general solutions for the ramification problem in the situation calculus. Using previously developed modularity techniques, we show that the basic variant of the inductive situation calculus without ramification rules is equivalent to Reiter-style situation calculus.

A Syntax-based Approach to Reasoning about Action and Belief Update

In this paper, we introduce an alternative approach to reasoning about action. The approach provides a solution to the frame and the ramification problem in a uniform manner. The approach involves keeping a (syntax-based) model of the world that is updated when actions are performed. Our approach is similar to the STRIPS system in which formulas are deleted and added as effects of an action. The presented framework however does not suffer from STRIPS' limitations in expressivity.

Three problems in logic‐based knowledge representation

PurposeThe purpose of this article is to give a non‐technical overview of some of the technical progress made recently on tackling three fundamental problems in the area of formal knowledge representation/artificial intelligence. These are the Frame Problem, the Ramification Problem, and the Qualification Problem. The article aims to describe the development of two logic‐based languages, the Event Calculus and Modular‐E, to address various aspects of these issues. The article also aims to set this work in the wider context of contemporary developments in applied logic, non‐monotonic reasoning and formal theories of common sense.Design/methodology/approachThe study applies symbolic logic to model aspects of human knowledge and reasoning.FindingsThe article finds that there are fundamental interdependencies between the three problems mentioned above. The conceptual framework shared by the Event Calculus and Modular‐E is appropriate for providing principled solutions to them.Originality/valueThis article provides an overview of an important approach to dealing with three fundamental issues in artificial intelligence.

The ramification problem in temporal databases: Changing beliefs about the past

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumption that fluents persist, and because actions have effects on the next situation only. In this paper we provide a solution to the ramification problem based on an extension of the situation calculus and the work of McCain and Turner. More specifically, we study the case where the effects of an action refer to the past, a particularly complex problem.

Actions with Duration and Constraints: The Ramification Problem in Temporal Databases

The ramification problem is a hard and ever present problem in systems exhibiting a dynamic behavior. The area of temporal databases in particular is still lacking satisfactory solutions to the ramification problem. In this paper, we address the ramification problem based on causal relationships that take time into account. We study the problem for both instantaneous actions and actions with duration. The proposed solution advances previous work by considering actions with effects occurring in any of the possible future situations resulting from an action's execution.

The Qualification Problem: A solution to the problem of anomalous models

Intelligent agents in open environments inevitably face the Qualification Problem: The executability of an action can never be predicted with absolute certainty; unexpected circumstances, albeit unlikely, may at any time prevent the successful performance of an action. Reasoning agents in real-world environments rely on a solution to the Qualification Problem in order to make useful predictions but also to explain and recover from unexpected action failures. Yet the main theoretical result known today in this context is a negative one: While a solution to the Qualification Problem requires to assume away by default abnormal qualifications of actions, straightforward minimization of abnormality falls prey to the production of anomalous models. We present an approach to the Qualification Problem which resolves this anomaly. Anomalous models are shown to arise from ignoring causality, and they are avoided by appealing to just this concept. Our theory builds on the established predicate logic formalism of the Fluent Calculus as a solution to the Frame Problem and to the Ramification Problem in reasoning about actions. The monotonic Fluent Calculus is enhanced by a default theory in order to obtain the nonmonotonic approach called for by the Qualification Problem. The approach has been implemented in an action programming language based on the Fluent Calculus and successfully applied to the high-level control of robots.

A Trajectory Approach to Causality

In this paper we propose a new approach to address the ramification problem in common-sense reasoning about action and change. We contrast the methods of McCain and Turner, Thielscher and Sandewall and, based on some of the limitations they encounter, we introduce a trajectory-based approach which keeps a history of the states through which a system evolves to characterise its dynamical state. We furnish an underlying state-transition semantics and a logic that admits an expressive, dynamical account of some typical scenarios which encounter modelling difficulties in the other approaches mentioned.

NON-DETERMINISM AND UNCERTAINTY IN THE SITUATION CALCULUS

The purpose of this article is to extend the situation calculus, a logical framework for the specification of theories of action and change, with actions that have a non-deterministic or uncertain nature. Our approach is based upon the idea that actions may have a deterministic component, and a probabilistic component. For example, the act of flipping a coin has a deterministic component (the actual coin toss) and an uncertain component (the outcome). We extend the language of the situation calculus in order to make explicit this distinction between these two action components. Furthermore, we provide means to reason about the outcomes of processes specified only in terms of deterministic action components (which we call behaviors). In particular, we show how one can compute the probability that some fluent will hold after specific behavior is realized. An important feature of our approach is that the syntactic and semantic structure of actions and situations is independent of the decomposition of actions into deterministic and uncertain components. Thus, we inherit solutions to the frame problem ramification problem, etc.

Integrating actions and state constraints: A closed-form solution to the ramification problem (sometimes)

Integrating actions and state constraints is a central problem in knowledge representation. State constraints are commonly used to represent the relationship between objects in the world. When a representation of action is integrated, state constraints implicitly define indirect effects of actions and impose further preconditions on the performance of actions. Thus, a semantically correct integration of actions and state constraints must address the ramification and qualification problems, as well as the frame problem. In this paper we achieve such an integration for a syntactically restricted class of situation calculus theories. This paper presents two major technical contributions. The first contribution is an axiomatic closed-form solution to the frame, ramification and qualification problems for a common class of theories. The solution is presented in the form of an automatable procedure that compiles a syntactically restricted set of situation calculus ramification constraints and effect axioms into a set of successor state axioms. The second major contribution of this paper is an independent semantic justification for this closed-form solution. In particular, we present a semantic specification for a solution to the frame and ramification problems in terms of a prioritized minimization policy, and show that the successor state axioms of our closed-form solution adhere to this specification. Observing that our minimization policy is simply an instance of prioritized circumscription, we exploit results of Lifschitz (1985) on computing circumscription to show that computing the prioritized circumscription yields our successor state axioms. In the special case where there are no ramification constraints, computing the circumscription yields Reiter's (1991) earlier successor state axiom solution to the frame problem.

Perceiving and Reasoning about a Changing World

A rational agent (artificial or otherwise) residing in a complex changing environment must gather information perceptually, update that information as the world changes, and combine that information with causal information to reason about the changing world. Using the system of defeasible reasoning that is incorporated into the OSCAR architecture for rational agents, a set of reason‐schemas is proposed for enabling an agent to perform some of the requisite reasoning. Along the way, solutions are proposed for the Frame Problem, the Qualification Problem, and the Ramification Problem. The principles and reasoning described have all been implemented in OSCAR.

On the Treatment of the Ramification Problem with Transaction Logic

In the planning area, the reasoning about action is important, because the plans are made by a sequence of actions. Each action has its effects that need to be treated and fonnalized in some logical framework.The use of Transaction Logic (TR) makes possible a perfect definition of fluents to treat the effects of the ramification problem, because the TR works with database controlled by transition and data oracles. The TR has a powerful set of inference rules that makes possible its implementation in PROLOG, like SLD-resolution, allowing the application of effects in a computational way.

Reasoning about actions: steady versus stabilizing state constraints

In formal approaches to commonsense reasoning about actions, the Ramification Problem denotes the problem of handling indirect effects which implicitly derive from so-called state constraints. We pursue a new distinction between two kinds of state constraints which will be proved crucially important for solving the general Ramification Problem. Steady constraints never, not even for an instant, cease being in force. As such they give rise to truly instantaneous indirect effects of actions. Stabilizing state constraints, on the other hand, may be suspended for a short period of time after an action has occurred. Indirect effects deriving from these constraints materialize with a short lag. This hitherto neglected distinction is shown to have essential impact on the Ramification Problem: if stabilizing state constraints interact, then approaches not based on so-called causal propagation prove defective. But causal propagation, too, is shown to risk producing anomalous models, in case steady and stabilizing indirect effects are propagated indiscriminately. Motivated by these two observations, we improve the theory of causal relationships and its Fluent Calculus axiomatization, which both are methods of causal propagation, so as to properly handle the distinction between steady and stabilizing constraints.

Causality as a key to the frame problem

In a formal description of actions and changes, there is a famous problem known as the “frame problem”. In its generalized form, the frame problem is that it is practically impossible to describe, or infer, all the necessary preconditions (qualification problem) and all the possible consequences of a given action (ramification problem). It is not only difficult to determine what changes and what does not change. It is also difficult to determine what is relevant. Our observations show that even humans cannot solve the frame problem. Humans simply behave as if there were no such problem. The solution is to use some kind of heuristics. We claim that the notion of causality is the heuristics necessary to solve the frame problem. The most important working hypothesis is that causality is not a logical relation. As one of the heuristics it is subject to change as more information becomes available. There are two kinds of nonmonotonicity in the reasoning of changes: one due to a lack of information, the other due to change itself. We need separate mechanisms for them because they are used in combination to solve the frame problem.

Ramification and causality

In formal systems for reasoning about actions, the ramification problem denotes the problem of handling indirect effects. These effects are not explicitly represented in action specifications but follow from general laws describing dependencies among components of the world state. An adequate treatment of indirect effects requires a suitably weakened version of the general law of persistence. It also requires a method to avoid unintuitive changes suggested by the aforementioned dependency laws. We propose a solution to the ramification problem that uses directed relations between two single effects, stating the circumstances under which the occurrence of the first causes the second. We argue for the necessity of an approach based on causality by elaborating the limitations of common paradigms employed to handle ramifications—the principle of categorization and the policy of minimal change. Our abstract solution is realized on the basis of a particular action calculus, namely, the fluent calculus.

A new method for reasoning about action

Reasoning about action is an important aspect of common sense reasoning and planning. It gives rise to three classical problems: the frame problem, the qualification problem and the ramification problem. Existing approaches cannot deal with these problems efficiently. This paper presents a new method which uses the stratified ATMS for reasoning about action to overcome the limitations of these approaches.

Turning an Action Formalism Into a Planner—a Case Study

The paper describes a case study that explores the idea of building a planner with a neat semantics of the plans it produces, by choosing some action formalism that is for the planning application and building the planner accordingly. In general - and particularly so for the action formalism used in this study, which is quite expressive - this strategy is unlikely to yield fast and efficient planners if the formalism is used naively. Therefore, we adopt the idea that the planner approximates the theoretically ideal plans, where the approximation gets the closer, the more run time the planner is allowed. As the particular formalism underlying our study allows a significant degree of uncertainty to be modeled and copes with the ramification problem, we end up in a planner that is functionally comparable to modern anytime uncertainty planners, yet based on a neat formal semantics.

On the disc theorem

On the disc theorem