The notion of time is ubiquitous in any activity that requires intelligence. In particular, several important notions like change, causality, action are described in terms of time. Therefore, the representation of time and reasoning about time is of crucial importance for many Artificial Intelligence systems. Specifically during the last 10 years, it has been attracting the attention of many AI researchers. In this survey, the results of this work are analysed. Firstly, Temporal Reasoning is defined. Then, the most important representational issues which determine a Temporal Reasoning approach are introduced: the logical form on which the approach is based, the ontology (the units taken as primitives, the temporal relations, the algorithms that have been developed,. . . ) and the concepts related with reasoning about action (the representation of change, causality, action,. . . ). For each issue the different choices in the literature are discussed. 1 Introduction The notion of time i...

It is important to remain uncertain, of observation, model and law. For the Fermi Summer School, Criticisms Requested email : lenny@maths.ox.ac.uk, Contents 1

This article discusses the problems and difficulties, the results so far, and some improvements in logic and logical languages that may be required to formalize common sense. Fundamental conceptual advances are almost certainly required. The object of the paper is to get more help for AI from philosophical logicians. Some of the requested help will be mostly philosophical and some will be logical. Likewise the concrete AI approach may fertilize philosophical logic as physics has repeatedly fertilized mathematics.

The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed (“seeing”) or was actively manipulated (“doing”). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. Causal knowledge underlies our ability to predict future events, to explain the occurrence of present events, and to achieve goals by means of actions. Thus, causal knowledge belongs to one of our most central cognitive competencies. However, the nature of causal knowledge has been debated. A number of philosophers and

1 Foreign metaphysics and the benefits of travel As objects go, foreigners are a pretty respectable bunch. They’re not figments of our collective imagination, or social constructions, or useful fictions. They’re not mind-dependent, and they don’t disappear when we don’t keep an eye on them. Our ‘folk theory ’ about foreigners isn’t subject to some global error, and the term ‘foreigner ’ certainly manages to refer. Some of our beliefs about foreigners are mistaken, no doubt, but only by failing to accord, case-by-case, with the objective reality to which they are certainly answerable. There are many facts still to be discovered about foreigners, such as their precise distribution in space and time. Moreover, these are matters for scientific study. And so on. In a nutshell, foreigners are as real as we are. Yet think of the discovery each of us made when, minds broadened by travel, we realised that foreigners themselves use the very same concept, but apply it to us! What we learnt (at that unsettling moment) was that the distinction between them and us, foreigners and compatriots, isn’t as objective as we’d assumed. It is a distinction drawn ‘from a perspective’—that of a local speech community, embedded in a tribal population. There are objective divisions in the world, of course, but not the asymmetric distinction that each side sees from where it stands. God sees us as Afghanis, Zimbabweans, and many things in between, perhaps, but not as locals and foreigners. 1 So, the reality of foreigners notwithstanding, there’s a sense in which foreignness is a less objective matter than we used to think. Perspectivity of this kind raises important philosophical issues. Some are general issues: How is the relevant notion of perspective best characterised? Is it one phenomena or

To be published in Behavioral and Brain Sciences (in press)

presented at

We propose to extend the ontology of logical AI to include approximate objects, approximate predicates and approximate theories. Besides the ontology we propose new representation and reasoning techniques, especially for formally relating different approximate theories of the same phenomena. Formal physical science theories treat well-defined objects in welldefined, or at least well-axiomatized, domains. Most logical AI theories have resembled scientific theories in this respect. However, human-level AI also requires reasoning about approximate entities, as we shall see. Approximate predicates can't have complete if-and-only-if definitions and usually don't even have de nite extensions. Some approximate concepts can be refined by learning more and some by defining more and some by both, but it isn't possible in general to make them well-defined. Approximate concepts are essential for representing common sense knowledge and doing common sense reasoning. Assertions involving appro...

We give a brief introduction of Hume’s epistemology and his penetrating analysis of causality. It is pointed out that there are some flaws in his epistemology and his theory of causation. Alternative theories of causation are then briefly introduced. Partly inspired by Hume’s analysis of necessary connexion, we present a new argument of causation. We argue that Hume’s removing necessary connexion from causality can help to provide a promising way to unify the law of causality and indeterminism. We then propose a generalized principle of causality, according to which there are two kinds of causes: concrete causes and universal causes, and correspondingly there are two kinds of effects: lawful events and random events. Each actual effect is composed of both lawful element and random element. A detailed analysis of the motion of objects is also given to support the new principle. 1.

Abstract Two arguments due to Russell are examined, and found to show that the notion of causation as full determination doesn’t mesh easily with deterministic global physics and the distinction between effective and ineffective strategies. But a local notion of causation as involving a certain kind of counterfactual dependence is, I argue, compatible with Russell’s conclusions. I defend it from a resurgent form of Russell’s microphysical determinism argument by making some mildly contentious claims about the autonomy of the events posited by the special sciences. 1 Russell’s Arguments Russell (1913) takes the relation of causation to be a relation of determination: c causes e just when c determines e to occur. This relation is supposed to be asymmetric and plausibly transitive as well. The fundamental law of causality is supposed to be that every event has a sufficient cause, one that is guaranteed to bring that event about and in fact did so. This intuition about the deterministic nature of causation is not a Rus-sellian idiosyncrasy: it originates in Hume’s ‘constant conjunction ’ regularity analysis