Agent Theories, Architectures, and Languages: A Survey <s> Introduction <s> Researchers using epistemic logic as a formal framework for studying knowledge properties of AI systems often interpret the knowledge formula K(x, φ) to mean that machine x encodes φ in its state as a syntactic formula or can derive it inferentially. By defining K(x, φ), instead, in terms of the correlation between the state of the machine and that of its environment, the formal properties of modal system S5 can be satisfied without having to store representations of formulas as data structures. In this paper, we apply the correlational definition of knowledge to machines with composite structure. In particular, we describe how epistemic properties of synchronous digital machines can be analyzed, starting at the level of gates and delays, by modeling the machine's components as agents in a multi-agent system and reasoning about the flow of information among them. We also introduce Rex, a language for recursively computing machine descriptions, and illustrate how it can be used to construct machines with provable knowledge properties. <s> BIB001 </s> Agent Theories, Architectures, and Languages: A Survey <s> Introduction <s> Cognitive Science attempts to study entities which in some sense possess beliefs, intentions, desires etc. The preferred term in this thesis for such entities is agents. An attempt at a scientific analysis of agents however throws up a number of questions. What are the right concepts to describe agents? What kinds of formal notations permit perspicuous reasoning about agents? What can be said about the architecture and construction of agents? The introductory section of this thesis discusses these questions in some detail, using Dennett's ideas about intentional systems [Den87] as a point of departure. I then examine a number of studies from Artificial Intelligence, Logic, Natural Language semantics and Philosophy which give shape to the current state of the art in agent theory. This leads to the development of a mathematical model of multi-object/agent interaction, which I call the SRS-model (SRS - Synchronous Reactive Systems). I demonstrate the adequacy of the model by using it to formalise learning and games scenarios. Next, various logics are introduced which capture input-output and then belief-desire level descriptions of agents. These logics are given an SRS-model semantics, and adequacy is shown by modelling a psychological experiment (an agent in a Skinner Box). It is shown how the example can be formally analysed in terms of mechanisms and architecture at the SRS-level (including a computer simulation); at the level of behaviourism (using a temporal logic - APTL); and at an intentional level (using an epistemic-conative temporal logic ECTL). Some remarks are made about extending the analysis to multi-agent situations involving co-operation, competition and dialogue. Finally, a detailed survey is made of the main mathematical and logical resources available to the style of formal cognitive science advocated in this thesis. I include modal logic, including its epistemic and doxastic variants; the extension of modal logic to dynamic logic; and the various approaches recently developed in the logic of time. <s> BIB002
One way of defining AI is by saying that it is the subfield of computer science which aims to construct agents that exhibit aspects of intelligent behaviour. One view, which would nowadays be regarded as extreme by many AI researchers, is that these agents will recreate intelligent human behaviour in all respects; a perhaps more widely held view is that even if human intelligence is out of the question, (at least for the time being), it would nevertheless be useful to be able to build agents that can exhibit some aspects of intelligent human behaviour. The notion of an agent is thus central to AI. It is perhaps surprising, therefore, that until the mid to late 1980s, researchers from mainstream AI gave relatively little consideration to the issues surrounding agent synthesis. Since then, however, there has been a marked flowering of interest in the subject, and the concept of an 'agent' has been adopted by a variety of subdisciplines of AI and mainstream computer science. One now hears of 'agents' in software engineering, data communications and concurrent systems research, as well as robotics, AI, and distributed AI. A recent article in a British national daily paper made the following prediction: 'Agent-based computing (ABC) is likely to be the next significant breakthrough in software development'  . What objects can be described by the intentional stance? As it turns out, more or less anything can. In his doctoral thesis, Seel showed that even very simple, automata-like objects can be consistently ascribed intentional descriptions BIB002 ; similar work by Rosenschein and Kaelbling, (albeit with a different motivation), arrived at a similar conclusion BIB001 . For example, consider a light switch: 'It is perfectly coherent to treat a light switch as a (very cooperative) agent with the capability of transmitting current at will, who invariably transmits current when it believes that we want it transmitted and not otherwise; flicking the switch is simply our way of communicating our desires'. [121, p6] And yet most adults would find such a description absurd -perhaps even infantile. Why is this? The answer seems to be that while the intentional stance description is perfectly consistent with the observed behaviour of a light switch, and is internally consistent, '… it does not buy us anything, since we essentially understand the mechanism sufficiently to have a simpler, mechanistic description of its behaviour'. [121, p6] Put crudely, the more we know about a system, the less we need to rely on animistic, intentional explanations of its behaviour. However, with very complex systems, even if a complete, accurate picture of the system's architecture and working is available, a mechanistic, design stance explanation of its behaviour may not be practicable. Consider a computer. Although we might have a complete technical description of a computer available, it is hardly practicable to appeal to such a description when explaining why a menu appears when we click a mouse on an icon. In such situations, it may be more appropriate to adopt an intentional stance description, if that description is consistent, and simpler than the alternatives. The intentional notions are thus abstraction tools, which provide us with a convenient and familiar way of describing, explaining, and predicting the behaviour of complex systems. Being an intentional system seems to be a necessary condition for agenthood, but is it a sufficient condition? In his recent Master's thesis, Shardlow trawled through the literature of cognitive science and its component disciplines in an attempt to find a unifying concept that underlies the notion of agenthood. He was forced to the following conclusion: 'Perhaps there is something more to an agent than its capacity for beliefs and desires, but whatever that thing is, it admits no unified account within cognitive science'.  So, an agent is a system that is most conveniently described by the intentional stance; one whose simplest consistent description requires the intentional stance. Before proceeding, it is worth considering exactly which attitudes are appropriate for representing agents. For the purposes of this survey, the two most important categories are information attitudes and pro-attitudes: information attitudes Thus information attitudes are related to the information that an agent has about the world it occupies, whereas pro-attitudes are those that in some way guide the agent's actions. Precisely which combination of attitudes is most appropriate to characterise an agent is, as we shall see later, an issue of some debate. However, it seems reasonable to suggest that an agent must be represented in terms of at least one information attitude, and at least one pro-attitude. Note that pro-and information attitudes are closely linked, as a rational agent will make make choices and form intentions, etc., on the basis of the information it has about the world. Much work in agent theory is concerned with sorting out exactly what the relationship between the different attitudes is. The next step is to investigate methods for representing and reasoning about these intentional notions.