Representing defeasibility is an important issue in common sense reasoning. In reasoning about action and change, this issue becomes more difficult because domain and action related defeasible information may conflict with general inertia rules. Furthermore, different types of defeasible information may also interfere with each other during the reasoning. In this paper, we develop a prioritized logic programming approach to handle defeasibilities in reasoning about action. In particular, we propose three action languages AT ^ 0 , AT ^ 1 and AT ^ 2 which handle three types of defeasibilities in action domains named defeasible constraints, defeasible observations and actions with defeasible and abnormal effects respectively. Each language with a higher superscript can be viewed as an extension of the language with a lower superscript. These action languages inherit the simple syntax of A language but their semantics is developed in terms of transition systems where transition functions are defined based on prioritized logic programs. By illustrating various examples, we show that our approach eventually provides a powerful mechanism to handle various defeasibilities in temporal prediction and postdiction. We also investigate semantic properties of these three action languages and characterize classes of action domains that present more desirable solutions in reasoning about action within the underlying action languages.
Although the work presented in @cite_2 provided a natural way to represent causality in reasoning about action, there were several restrictions in this action theory. First, due to technical restrictions, only normal defaults or defaults without justifications are the suitable forms to represent causal rules in problem domains. Second, this action theory did not handle the other two major defeasibilities - defeasible observations and actions with defeasible and abnormal effects.
@cite_2
2017479918
Recent research on reasoning about action has shown that the traditional logic form of domain constraints is problematic to represent ramifications of actions that are related to causality of domains. To handle this problem properly, as proposed by some researchers, it is necessary to describe causal relations of domains explicitly in action theories. In this paper, we address this problem from a new point of view. Specifically, unlike other researchers viewing causal relations as some kind of inference rules, we distinguish causal relations between defeasible and non-defeasible cases. It turns out that a causal theory in our formalism can be specified by using Reiter's default logic. Based on this idea, we propose a causality-based minimal change approach for representing effects of actions, and argue that our approach provides more plausible solutions for the ramification and qualification problems compared with other related work. We also describe a logic programming approximation to compute causal theories of actions which provides an implementational basis for our approach. We present here a knowledge representation language, where defeasible and non-defeasible rules can be expressed. The language has two different negations: classical negation, which is represented by the symbol “∼” used for representing contradictory knowledge; and negation as failure, represented by the symbol “not” used for representing incomplete information. Defeasible reasoning is done using a argumentation formalism. Thus, systems for acting in a dynamic domain, that properly handle contradictory and or incomplete information can be developed with this language. An argument is used as a defeasible reason for supporting conclusions. A conclusion q will be considered justified only when the argument that supports it becomes a justification. Building a justification involves the construction of a nondefeated argument A for q. In order to establish that A is a non-defeated argument, the system looks for counterarguments that could be defeaters for A. Since defeaters are arguments, there may exist defeaters for the defeaters, and so on, thus requiring a complete dialectical analysis. The system also detects, avoids, circular argumentation. The language was implemented using an abstract machine defined and developed as an extension of the Warren Abstract Machine (wam). The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query @math will succeed when there is an argument @math for @math that is warranted, i.e. the argument @math that supports @math is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents. In this dissertation I present a formal approach to defeasible reasoning. This mathematical approach is based on the notion of specificity introduced by Poole and the general theory of warrant as presented by Pollock. General background information on the subject of Nonmonotonic Reasoning is presented and some of the shortcomings of existing systems are analyzed. We believe that the approach presented here represents a definite improvement over past systems. The main contribution of this thesis is a formally precise, elegant, clean, well-defined system which exhibits a correct behavior when applied to the benchmark examples in the literature. Model-theoretic semantical issues have been addressed. The investigation on the theoretical issues has aided the study of how this kind of reasoner can be realized on a computer. An interpreter of a restricted language, an extension of Horn clauses with defeasible rules, has been implemented. Finally, the implementation details are discussed. The nonmonotonic causal logic defined in this paper can be used to represent properties of actions, including actions with conditional and indirect effects, nondeterministic actions, and concurrently executed actions. It has been applied to several challenge problems in the theory of commonsense knowledge. We study the relationship between this formalism and other work on nonmonotonic reasoning and knowledge representation, and discuss its implementation, called the Causal Calculator. This paper shows how action theories, expressed in an extended version of the language , can be naturally encoded using Prioritized Default Theory. We also show how prioritized default theory can be extended to express preferences between rules. This extension provides a natural framework to introduce different types of preferences in action theories—preferences between actions and preferences between final states. In particular, we demonstrate how these preferences can be expressed within extended prioritized default theory. We also discuss how this framework can be implemented in terms of answer set programming. Abstract We present an abstract framework for default reasoning, which includes Theorist, default logic, logic programming, autoepistemic logic, non-monotonic modal logics, and certain instances of circumscription as special cases. The framework can be understood as a generalisation of Theorist. The generalisation allows any theory formulated in a monotonic logic to be extended by a defeasible set of assumptions. An assumption can be defeated (or “attacked”) if its “contrary” can be proved, possibly with the aid of other conflicting assumptions. We show that, given such a framework, the standard semantics of most logics for default reasoning can be understood as sanctioning a set of assumptions, as an extension of a given theory, if and only if the set of assumptions is conflict-free (in the sense that it does not attack itself) and it attacks every assumption not in the set. We propose a more liberal, argumentation-theoretic semantics, based upon the notion of admissible extension in logic programming. We regard a set of assumptions, in general, as admissible if and only if it is conflict-free and defends itself (by attacking) every set of assumptions which attacks it. We identify conditions for the existence of extensions and for the equivalence of different semantics. This paper relates the Defeasible Logic Programming (DeLP) framework and its semantics SEMDeLP to classical logic programming frameworks. In DeLP, we distinguish between two different sorts of rules: strict and defeasible rules. Negative literals (∼A) in these rules are considered to represent classical negation. In contrast to this, in normal logic programming (NLP), there is only one kind of rules, but the meaning of negative literals (not A) is different: they represent a kind of negation as failure, and thereby introduce defeasibility. Various semantics have been defined for NLP, notably the well-founded semantics (WFS) (van , Proceedings of the Seventh Symposium on Principles of Database Systems, 1988, pp. 221-230; J. ACM 38 (3) (1991) 620) and the stable semantics Stable (Gelfond and Lifschitz, Fifth Conference on Logic Programming, MIT Press, Cambridge, MA, 1988, pp. 1070-1080; Proceedings of the Seventh International Conference on Logical Programming, Jerusalem, MIT Press, Cambridge, MA, 1991, pp. 579-597).In this paper we consider the transformation properties for NLP introduced by Brass and Dix (J. Logic Programming 38(3) (1999) 167) and suitably adjusted for the DeLP framework. We show which transformation properties are satisfied, thereby identifying aspects in which NLP and DeLP differ. We contend that the transformation rules presented in this paper can help to gain a better understanding of the relationship of DeLP semantics with respect to more traditional logic programming approaches. As a byproduct, we obtain the result that DeLP is a proper extension of NLP. For digital interactive distributed systems, the timing of their events and the causality between their events are key issues. In real-time applications of embedded software systems, timing properties are essential, such as the response times of reactions depending on the precise timing of the input events. In a highly abstract view, a digital system can be represented by a set of events annotated by their timing. Sets of timed events labeled by actions represent the observations about systems. An essential property that helps to understand distributed interactive systems and a way to reason about their event flow is causality . Causality addresses the questions under which conditions certain events must, may or must not happen. Causality addresses the logical dependencies between the events in systems. Strictly speaking, causality reflects the logical essence in the event and action flow of systems. Causality is closely related to time. In particular, we study in the following the relationship between causality and the timing of input and output events, as well as its relationship to the granularity of time. We deal, in particular, with the problem of time abstraction and the precise timing of events. We show how causality and time form the basis of inductive reasoning, in particular in the case of dependencies in communication cycles (‘feedback’) and how we can work with time in models of distributed systems with a flexible choice of local clocks and local timing. We address the problem of visually detecting causal events and fitting them together into a coherent story of the action witnessed by the camera. We show that this can be done by reasoning about the motions and collisions of surfaces, using high-level causal constraints derived from psychological studies of infant visual behavior. These constraints are naive forms of basic physical laws governing substantiality, contiguity, momentum, and acceleration. We describe two implementations. One system parses instructional videos, extracting plans of action and key frames suitable for storyboarding. Since learning will play a role in making such systems robust, we introduce a new framework for higher-order hidden Markov models and demonstrate its use in a second system that segments stereo video into actions in near real-time. Rather than attempt accurate low-level vision, both systems use high-level causal analysis to integrate fast but sloppy pixel-based representations over time. The output is suitable for summary, indexing, and automated editing. We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The frame-work features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper. We examine the meaning of causality in calculi for mobile processes like the π-calculus, and we investigate the relationship between interleaving and causal semantics for such calculi. We separate two forms of causal dependencies on actions of π-calculus processes, called subject and object dependencies: The former originate from the nesting of prefixes and are propagated through interactions among processes (they are the only form of causal dependencies present in CCS-like languages); the latter originate from the binding mechanisms on names. We propose a notion of causal bisimulation which distinguishes processes which differ for the subject or for the object dependencies. We show that this causal equivalence can be reconducted to, or implemented into, the ordinary interleaving observation equivalence. We prove that our encoding is fully abstract w.r.t. the two behavioural equivalences. This allows us to exploit the simpler theory of the interleaving semantics to reason about the causal one. In [San94b] a similar programme is carried out for location bisimulation [BCHK91], a non-interleaving spatial-sensitive (as opposed to causal-sensitive) behavioural equivalence. The comparison between the encodings of causal bisimulation in this paper, and of location bisimulation in [San94b], evidences the similarities and the differences between these two equivalences. Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story plot of the video. The storyline of a video describes causal relationships between actions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are represented in terms of spatio-temporal constraints. We formulate an Integer Programming framework for action recognition and storyline extraction using the storyline model and visual groundings learned from training data. Research in discourse analysis, story understanding, and user modeling for expert systems has shown great interest in plan recognition problems. In a plan recognition problem, one is given a fragmented description of actions performed by one or more agents, and expected to infer the overall plan or scenario which explains those actions. This thesis develops the first formal description of the plan recognition process. Beginning with a reified logic of events, the thesis presents a scheme for hierarchically structuring a library of event types. A semantic basis for non-deductive inference, called "minimum covering entailment", justifies the conclusions that one may draw from a set of observed actions. Minimum covering entailment is defined by delineating the class of models in which the library is complete and the set of unrelated observations is minimized. An equivalent proof theory forms a preliminary basis for mechanizing the theory. Equivalence theorems between the proof and model theories are presented. Minimum covering entailment is related to a formalism for non-monotonic inference known as "circumscription". Finally, the thesis describes a number of algorithms which correctly implement the theory, together with a discussion of their complexity. The theory is applied to a number of examples of plan recognition, in domains ranging from an operating system advisor to the theory of speech acts. The thesis shows how problems of medical diagnosis, a similar kind of non-deductive reasoning, can be cast in the framework, and an example previously solved by a medical expert system is worked out in detail. The analyses provides a firm theoretical foundation for much of what is loosely called "frame based inference", and directly accounts for problems of ambiguity, abstraction, and complex temporal interactions, which were ignored by previous work. The framework can be extended to handle difficult phenomena such as errors, and can also be restricted in order to improve its computational properties in specialized domains. 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect. An abstract framework for structured arguments is presented, which instantiates Dung's (‘On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming, and n-Person Games’, Artificial Intelligence, 77, 321–357) abstract argumentation frameworks. Arguments are defined as inference trees formed by applying two kinds of inference rules: strict and defeasible rules. This naturally leads to three ways of attacking an argument: attacking a premise, attacking a conclusion and attacking an inference. To resolve such attacks, preferences may be used, which leads to three corresponding kinds of defeat: undermining, rebutting and undercutting defeats. The nature of the inference rules, the structure of the logical language on which they operate and the origin of the preferences are, apart from some basic assumptions, left unspecified. The resulting framework integrates work of Pollock, Vreeswijk and others on the structure of arguments and the nature of defeat and extends it... Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers. The model updates the states of the entities by executing learned action operators. Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives. In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action. Examples include selecting offers, prices, advertisements, or emails to send to consumers, as well as the problem of determining which medication to prescribe to a patient. While there is a growing body of literature devoted to this problem, most existing results are focused on the case where data comes from a randomized experiment, and further, there are only two possible actions, such as giving a drug to a patient or not. In this paper, we study the offline multi-action policy learning problem with observational data and where the policy may need to respect budget constraints or belong to a restricted policy class such as decision trees. We build on the theory of efficient semi-parametric inference in order to propose and implement a policy learning algorithm that achieves asymptotically minimax-optimal regret. To the best of our knowledge, this is the first result of this type in the multi-action setup, and it provides a substantial performance improvement over the existing learning algorithms. We then consider additional computational challenges that arise in implementing our method for the case where the policy is restricted to take the form of a decision tree. We propose two different approaches, one using a mixed integer program formulation and the other using a tree-search based algorithm. The Katsuno and Mendelzon theory of belief update has been proposed as a reasonable model for revising beliefs about a changing world. However, the semantics of update relies on information which is not readily available. We describe an alternative semantical view of update in which observations are incorporated into a belief set by: a) explaining the observation in terms of a set of plausible events that might have caused that observation; and b) predicting further consequences of those explanations. We also allow the possibility of conditional explanations. We show that this picture naturally induces an update operator under certain assumptions. However, we argue that these assumptions are not always reasonable, and they restrict our ability to integrate update with other forms of revision when reasoning about action. This paper describes a natural language system which improves its own performance through learning. The system processes short English narratives and is able to acquire, from a single narrative, a new schema for a stereotypical set of actions. During the understanding process, the system attempts to construct explanations for characters' actions in terms of the goals their actions were meant to achieve. When the system observes that a character has achieved an interesting goal in a novel way, it generalizes the set of actions they used to achieve this goal into a new schema. The generalization process is a knowledge-based analysis of the causal structure of the narrative which removes unnecessary details while maintaining the validity of the causal explanation. The resulting generalized set of actions is then stored as a new schema and used by the system to correctly process narratives which were previously beyond its capabilities.