Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Abstract The purpose of this paper is to discuss some organizational aspects of programs using the actor model of computation. In this paper we present an approach to modelling intelligence in terms of a society of communicating knowledge-based problem-solving experts. In turn each of the experts can be viewed as a society that can be further decomposed in the same way until the primitive actors of the system are reached. We are investigating the nature of the communication mechanisms needed for effective problem-solving by a society of experts and the conventions of civilized discourse that make this possible. In this way we hope eventually to develop a framework adequate for the discussion of the central issues of problem-solving involving parallel versus serial processing and centralization versus decentralization of control and information storage. This paper demonstrates how actor message passing can be used to understand control structures as patterns of passing messages in serial processing. This paper is a pre-requisite for successors which treat issues of parallelism and communication within the framework established here. The ability to analyze or synthesize any kind of control structure as a pattern of passing messages among the members of a society provides an important tool for understanding control structures. Ultimately, we hope to be able to characterize various control structures in common use by societies in terms of patterns of passing messages. This paper makes a small step in this direction by showing how to characterize familiar control structures such as iteration and recursion in these terms. <s> BIB001 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Representing and reasoning about the knowledge an agent (human or computer) must have to accomplish some task is becoming an increasingly important issue in artificial intelligence (AI) research. To reason about an agent's beliefs, an AI system must assume some formal model of those beliefs. An attractive candidate is the Deductive Belief model: an agent's beliefs are described as a set of sentences in some formal language (the base sentences), together with a deductive process for deriving consequences of those beliefs. In particular, a Deductive Belief model can account for the effect of resource limitations on deriving consequences of the base set: an agent need not believe all the logical consequences of his beliefs. In this paper we develop a belief model based on the notion of deduction, and contrast it with current AI formalisms for belief derived from Hintikka/Kripke possible-worlds semantics for knowledge. <s> BIB002 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Reasoning About Change presents a comprehensive approach to temporal reasoning in artificial intelligence. <s> BIB003 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> To date, reactive robot behavior has been achieved only through manual programming. This paper describes a new kind of plan, called a "universal plan", which can be synthesized automatically, yet generates appropriate behavior in unpredictable environments. In classical planning work, problems were posed with unique initial and final world states; in my approach a problem specifies only a goal condition. The planner is thus unable to commit to any specific future course of events but must specify appropriate reactions for anticipated situations. An alternative conception is that one universal plan compactly represents every classical plan. Which part of the universal plan is executed depends entirely on how the environment behaves at execution time. ::: ::: Universal plans are constructed from state-space operator schemas by a nonlinear planner. They explicitly identify predicates requiring monitoring at each moment of execution, and provide for sabotage, serendipity and failure without requiring replanning. <s> BIB004 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> A model of purely reactive planning is proposed based on the concept of reactive action packages. A reactive action package, or RAP, can be thought of as an independent entity pursuing some goal in competition with many others at execution time. The RAP processing algorithm addresses the problems of execution monitoring and replanning in uncertain domains with a single, uniform representation and control structure. Use of the RAP model as a basis for adaptive strategic planning is also discussed. <s> BIB005 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Abstract In the conclusion of [7] Halpern and Moses expressed their interest in a logical system in which one could talk about knowledge and belief (and belief about knowledge, knowledge about belief and so on). We investigate such systems. In the first part of the paper knowledge and belief, without time, are considered. Common knowledge and common belief are defined and compared. A logical system and a family of models are proposed, a completeness result is proved and a decision procedure described. In the second part of the paper, time is considered. Different notions of beliefs are distinguished, obeying different properties of persistence. One interpretation of belief which obeys a very strong persistence axiom is put forward and used in the analysis of the “wise men” puzzle. <s> BIB006 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Abstract : Discourses are fundamentally instances of collaboration behavior. We propose a model of the collaborative plans of agents achieving joint goals and illustrate the role of these plans in discourses. Three types of collaborative plans, called Shared Plans, are formulated for joint goals requiring simultaneous, conjoined or sequential actions on the part of the agents who participate in the plans and the discourse; a fourth type of Shared Plan is presented for the circumstance where two agents communicate, but only one acts. <s> BIB007 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> 1 Reasoning about Actions and Planning 2 Basic Assumptions and Limitations 3 SIPE and Its Representations 4 Hierarchical Planning as Differing Abstraction Levels 5 Constraints 6. The Truth Criterion 7 Deductive Causal Theories 8 Plan Critics 9 Resources: Reusable, Consumable, Temporal 10 Search 11 Replanning During Execution 12 Planning and Reactivity 13 Achieving Heuristic Adequacy 14 Comparison with Other Systems <s> BIB008 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Abstract We study the propositional model logic of knowledge and time for distributed systems. We consider a number of logics (ninety-six in all!), which vary according to the choice of language and the assumptions made on the underlying system. The major parameters in the language are whether there is a common knowledge operator, whether we reason about the knowledge of one or more than one processor, and whether our temporal operators are branching or linear. The assumptions on distributed systems that we consider are: whether or not processors forget, whether or not processors learn, whether or not time is synchronous, and whether or not there is a unique initial state in the system. We completely characterize the complexity of the validity problem for all the logics we consider. This paper focuses on lower bounds; a sequel will deal with the corresponding upper bounds. Typical results include a ∏-completeness result for the language with common knowledge with respect to systems where processors do not forget, and a corresponding non-elementary-time result for the language without common knowledge. It is shown that, in general, the assumption that processors do not forget or do not learn greatly increases the complexity of reasoning about knowledge and time. <s> BIB009 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> We consider the role played by the concept of action in AI. We first briefly summarize the advantages and limitations of past approaches to taking the concept as primitive, as embodied in the situation calculus and dynamic logic. We also briefly summarize the alternative, namely adopting a temporal framework, and point out its complementary advantages and limitations. We then propose a framework that retains the advantages of both viewpoints, and that ties the notion of action closely to that of knowledge. Specifically, we propose starting with the notion of time lines, and defining the notion of action as the ability to make certain choices among sets of time lines. Our definitions shed new light on the connection between time, action, knowledge and ignorance, choice-making, feasibility, and simultaneous reasoning about the same events at different levels of detail. <s> BIB010 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> How are we able to understand and anticipate each other in everyday life, in our daily interactions? Through the use of such "folk" concepts as belief, desire, intention, and expectation, asserts Daniel Dennett in this first full-scale presentation of a theory of intentionality that he has been developing for almost twenty years. We adopt a stance, he argues, a predictive strategy of interpretation that presupposes the rationality of the people - or other entities - we are hoping to understand and predict.These principles of radical interpretation have far-reaching implications for the metaphysical and scientific status of the processes referred to by the everday terms of folk psychology and their corresponding terms in cognitive science.While Dennett's philosophical stance has been steadfast over the years, his views have undergone successive enrichments, refinements, and extensions. "The Intentional Stance" brings together both previously published and original material: four of the book's ten chapters - its first and the final three - appear here for the first time and push the theory into surprising new territory. The remaining six were published earlier in the 1980s but were not easily accessible; each is followed by a reflection - an essay reconsidering and extending the claims of the earlier work. These reflections and the new chapters represent the vanguard of Dennett's thought. They reveal fresh lines of inquiry into fundamental issues in psychology, artificial intelligence, and evolutionary theory as well as traditional issues in the philosophy of mind.Daniel C. Dennett is Distinguished Arts and Sciences Professor at Tufts University and the author of "Brainstorms" and "Elbow Room." "The Intentional Stance," along with these works, is a Bradford Book. <s> BIB011 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Publisher Summary This chapter discusses temporal and modal logic. The chapter describes a multiaxis classification of systems of temporal logic. The chapter describes the framework of linear temporal logic. In both its propositional and first-order forms, linear temporal logic has been widely employed in the specification and verification of programs. The chapter describes the competing framework of branching temporal logic, which has seen wide use. It also explains how temporal logic structures can be used to model concurrent programs using non-determinism and fairness. The chapter also discusses other modal and temporal logics in computer science. The chapter describes the formal syntax and semantics of Propositional Linear Temporal Logic (PLTL). The chapter also describes the formal syntax and semantics for two representative systems of propositional branching-time temporal logics. <s> BIB012 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> This paper discusses the problem of action selection for an autonomous agent. We argue that the so-called ''situated agents'', which have been built in response to the limitations observed with classical planners, do not provide a satisfactory solution to this problem because of their lack of goals and run-time arbitration. We present a novel action selection theory which allows arbitration among goals and actions while producing fast and robust activity in a tight interaction loop with the environment. The theory models action slection as an emergent property of an activation/inhibition dynamics among the actions the agent can select and between the actions and the environment. A handful of global parameters make it possible to smoothly mediate between several action selection criteria. For example, one can balance goal-orientedness against situation-orientedness, bias towards ongoing plans (inertia) against adaptivity, thoughtfulness against speed, and adjust sensitivity to goal conflicts. <s> BIB013 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> 1. Logic 2. Information 3. Situation theory 4. Meaning and constraints 5. Some logical issues 6. Mental states 7. Perception and action 8. Situation semantics 9. Topics in situation semantics 10. Historical perspectives and future directions. <s> BIB014 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> As part of our work on agent-oriented programming,1 we are developing an agent description language. We describe an agent's “mental state” in terms of its knowledge, beliefs, desires, commitments, and abilities. Our goal is not to specify human knowledge, beliefs, etc., but rather to devise a more limited, precise language to facilitate programming agents and interagent communication. We believe that borrowing from our intuition about these commonsense terms will simplify the human designer's job. This article describes a preliminary version of our language. We define a temporal language with modal operators representing knowledge, belief, desire, commitment, and ability. This is a propositional language with quantification over time points and agents only. <s> BIB015 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> "Planning and Control" explores planning and control by reformulating the two areas in a common control framework, developing the corresponding techniques side-by-side, and identifying opportunities for integrating their ideas and methods. This book is organized around the central roles of prediction, observation, and computation control. The first three chapters deal with predictive models of physical systems based on temporal logic and the differential calculus. Chapter 4 and 5 present some basic concepts in planning and control, including controllability, observability, stability, feedback control, task reduction, conditional plans, and the relationship between goals and preferences. Chapters 6 and 7 consider issues of uncertainty, covering state estimation and the Kalman filter, stochastic dynamic programming, probabilistic modeling, and graph-based decision models. The remaining chapters investigate selected topics in time-critical decision making, adaptive control, and hybrid control architectures. Throughout, the reader is led to consider critical tradeoffs involving the accuracy of prediction, the availability of information from observation, and the costs and benefits of computation in dynamic environments. This book is useful to researchers in artificial intelligence and control theory, and others concerned with the design of complex applications in robotics, automated manufacturing, and time-critical decision support. <s> BIB016 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Temporal Reasoning and Planning 1.1 Introduction 1.1.1 Background: Actions as State Change 1.1.2 Temporally Explicit Representations 1.2 Representing Time 1.2.1 Temporal Logic 1.3 The Logic of Action 1.4 The Logic for Planning 1.4.1 Predicting the Future 1.4.2 Planning With a Temporal World Model 1.5 The Planning System 1.5.1 A Nonlinear Planning Algorithm 1.5.2 Assumptions About Temporal Extension 1.5.3 Discussion of the Algorithm 1.6 The Door-Latch Problem 1.7 Hierarchical Planning 1.8 Conclusions A Formal Theory of Plan Recognition and its Implementation Henry A. Kautz 2.1 Introduction 2.1.1 Background 2.1.2 Overview 2.1.3 Plan Recognition and the Frame Problem 2.2 Representing Event Hierarchies 2.2.1 The Language 2.2.2 Representation of Time, Properties, and Events 2.2.3 The Event Hierarchy 2.2.4 Example: The Cooking World 2.3 The Formal Theory of Recognition 2.3.1 Exhaustiveness Assumptions (EXA) 2.3.2 Disjointness Assumptions (DJA) 2.3.3 Component/Use Assumptions (CUA) 2.3.4 Minimum Cardinality Assumptions (MCA) 2.3.5 Example: the Cooking World 2.3.6 Circumscription and Plan Recognition 2.4 Examples 2.4.1 An Operating System 2.4.2 Medical Diagnosis 2.5 Algorithms for Plan Recognition 2.5.1 Directing Inference 2.5.2 Explanation Graphs 2.5.3 Implementing Component/Use Assumptions 2.5.4 Constraint Checking 2.5.5 Algorithms Planning With Simultaneous Actions and External Events Richard N. Pelavin 3.1 Introduction 3.1.1 Key Considerations 3.1.2 Overview 3.2 Representations That Treat Simultaneous Events 3.2.1 Interval Logic 3.2.2 A Model Based on Events 3.2.3 Branching time Models 3.2.4 Adapting the State-Change Model to Handle Simultaneous Events 3.3 The Semantic Model 3.3.1 The Interval Logic Structure 3.3.2 The Branching Time Structure 3.3.3 Plan Instances and Basic Actions 3.3.4 The Closeness Function 3.3.5 Composition and Interaction of Basic Action Instances 3.4 The Language 3.4.1 The Syntax: Additions to Interval Logic 3.4.2 Interpretations 3.5 Planning 3.5.1 Planner Correctness 3.5.2 The World Description 3.5.3 The Action Specifications 3.5.4 Plan Instance Composition 3.5.5 Persistence and Maintenance Plan Instances 3.6 A Planning Algorithm 3.6.1 The Input to the Planning Algorithm 3.6.2 The Algorithm 3.6.3 Planning Examples 3.6.4 Limitations of the Planning Algorithm 3.7 Discussion 3.7.1 Issues Outside the Scope of the Deductive Logic 3.7.2 Incomplete Descriptions and Obtaining Additional Information 3.7.3 Planning With an Incorrect World Description 3.7.4 Addressing the Frame Problem 3.8 Appendix A. the Semantic Model and Logical Language 3.8.1 The Semantic Model 3.8.2 The Syntax 3.8.3 The Interpretation of the Language 3.9 Appendix B. Proof Theory Abstraction in Planning Josh D. Tenenberg 4.1 Introduction 4.1.1 Inheritance Abstraction 4.1.2 Relaxed Model Abstraction 4.1.3 Macro Expansion 4.1.4 Outline 4.1.5 STRIPS 4.2 Inheritance Abstraction 4.2.1 Generalizing Inheritance 4.2.2 Predicate Mappings 4.2.3 Model Abstraction 4.2.4 Theory Abstraction 4.2.5 Theory Mappings 4.2.6 Proof-Theoretic Relationship Between Levels 4.2.7 Abstract STRIPS Systems 4.2.8 Downward Solution Property 4.2.9 Example 4.2.10 The Frame Problem 4.2.11 Comparison With Kautz 4.2.12 Related Research in Plan Generation 4.2.13 Summary 4.3 Abstraction Using Relaxed Models 4.3.1 ABSTRIPS 4.3.2 Planning With Inconsistent Systems 4.3.3 Restricted ABSTRIPS 4.3.4 Simple Restrictions 4.3.5 Relaxing the Simple Restrictions 4.3.6 Example 4.3.7 Upward Solution Property 4.3.8 The Monotonicity Property 4.3.9 Search 4.3.10 Related Research 4.3.11 Summary 4.4 Conclusion 4.4.1 Discrete Levels 4.4.2 Solution Properties 4.5 Proofs 4.5.1 Proofs for Inheritance Abstraction 4.5.2 Proofs for Relaxed Model Abstraction References Index <s> BIB017 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Abstract We review and re-examine possible-worlds semantics for propositional logics of knowledge and belief with three particular points of emphasis: (1) we show how general techniques for finding decision procedures and complete axiomatizations apply to models for knowledge and belief, (2) we show how sensitive the difficulty of the decision procedure is to such issues as the choice of modal operators and the axiom system, and (3) we discuss how notions of common knowledge and distributed knowledge among a group of agents fit into the possible-worlds framework, As far as complexity is concerned, we show, among other results, that while the problem of deciding satisfiability of an S5 formula with one agent is NP-complete, the problem for many agents is PSPACE-complete. Adding a distributed knowledge operator does not change the complexity, but once a common knowledge operator is added to the language, the problem becomes complete for exponential time. <s> BIB018 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> It has been argued that knowledge is a useful tool for designing and analyzing complex systems. The notion of knowledge that seems most relevant in this context is an external, zrzforrnatzorr-based notion that can be shown to satisfy all the axioms of the modal logic S5. The properties of this notion of knowledge are examined, and it is shown that they depend crucially. and in subtle ways, on assumptions made about the system and about the language used for describing knowledge. A formal model is presented in which one can capture various assumptions frequently made about systems, such as whether they are deterministic or nondeterministic, whether knowledge is cumulative (which means that processes never "forget"), and whether or not the "environment" affects the state transitions of the processes. It 1s then shown that under some assumptions about the system and the language, certain states of knowledge are not attainable and the axioms of S5 do not completely characterize the properties of knowledge; extra axioms are needed. Complete axiomatlzations for knowledge in a number of cases of interest are provided. <s> BIB019 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> As part of an effort to define a unified formal semantics for beliefs, desires and action, this paper sketches a model theory for the axiological aspects of agent theory: hedonic states, likes, goals and values. Particular attention is paid to modelling the intensity of likes. The main intuition underlying the model theory is that the axiological aspects of agent theory can be modelled through computational generalisations of physical dynamics. Computational analogues of force, mass and potential are offered. <s> BIB020 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> According to the utilitarian paradigm, an autonomous intelligent agent's interactions with the environment should be guided by the principle of expected utility maximization. We apply this paradigm to reasoning about an agent's physical actions and exploratory behavior in urgent, time-constrained situations. We model an agent's knowledge with a temporalized version of Kripke structures--as a set of branching time lines described by fluents, with accessibility relations holding among the states comprising the time lines. We describe how to compute utility based on this model which reflects the urgency that the environment imposes on time. Since the physical and exploratory actions that an agent could undertake transform the model of branching time lines in specific ways, the expected utilities of these actions can be computed, dictating rational tradeoffs among them depending on the agent's state of knowledge and the urgency of the situation. <s> BIB021 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> When multiple "agents" (robots, intelligent processes, people) share an environment they very often share resources; this requires coordination. Centralized coordination introduces a bottleneck into the system, so distributed coordination is often preferable. If agents can adopt new tasks dynamically then maximum flexibility is obtained when this coordination is done dynamically as well. This requires that agents be able to communicate with each other, and further that they be able to decide dynamically when and what to communicate. Shoham has proposed "agent oriented programming" as a method for programming intelligent, autonomous processes which interact in a shared environment. "Agents" are entities that may be modeled in terms of their "mental states", consisting of such components as beliefs, abilities, desires, and intentions. An agent's program gives its initial mental state and a set of rules which specify a transition function: given an agent's current state and input, these rules specify the agent's new mental state and output. Agents communicate by sending messages; these messages come in several types, where the types are drawn from speech act theory (requests, promises, etc.). ::: In this dissertation we present and discuss a new agent-oriented programming language, PLACA (which stands for PLAnning Communicating Agents). PLACA is a descendant of AGENT0, the first agent-oriented programming language; it addresses a particular shortcoming of AGENT0. Specifically, PLACA capitalizes on the planning abilities of agents. Assuming that all agents in a particular environment have at least elementary planning abilities, PLACA agents can make high-level requests of each other without worrying about the details of how a particular task will be carried out. In order to achieve this, agents' mental states have been expanded to include plans and intentions in addition to the beliefs and capabilities present in AGENT0 (obligations and decisions were dropped). The kinds of changes to the mental state which may be specified in an agent program have also been expanded. ::: We present a logic in which to represent the components of an agent's mental state, the programming language PLACA, and a description and discussion of a simple implemented PLACA interpreter. <s> BIB022 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> ions are relations between propositions, in which one is more abstract or general than another, let a be making a spaghetti-marinara dinner, and b be making a spaghetti dinner. In every possible world it is the case that a D b, so an intention to do a necessarily implies an intention to do b, if they are treated as normal modal operators. Normal modal operators are never abstraction free, but as we argue in the next section, intention must be. Another property of normal modal operators is that they are transparent to conjunction: L(a A b) =_ La A Lb. This means an intention to achieve a conjoined goal is 1Cohen and Levesque [9], in adopting the realism thesis, rely on claims about way an agent may change his beliefs about the connection between an intended proposition and its consequences to make their theory side-effect free. In their case, an agent who always believes that a D b is always true will incur the side-effect problem when intending a. Also, their theory will not be abstraction free or conjunction free: see below. <s> BIB023 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Since its publication, Essentials of Artificial Intelligence has been ::: adopted at numerous universities and colleges offering introductory AI ::: courses at the graduate and undergraduate levels. Based on the author's ::: course at Stanford University, the book is an integrated, cohesive ::: introduction to the field. The author has a fresh, entertaining writing ::: style that combines clear presentations with humor and AI anecdotes. At the ::: same time, as an active AI researcher, he presents the material ::: authoritatively and with insight that reflects a contemporary, first hand ::: understanding of the field. Pedagogically designed, this book offers a ::: range of exercises and examples. ::: ::: ::: Table of Contents ::: ::: 1 Introduction: What is AI? ::: 2 Overview ::: 3 Blind Search ::: 4 Heuristic Search ::: 5 Adversary Search ::: 6 Introduction to Knowledge Representation ::: 7 Predicate Logic ::: 8 First-Order Logic ::: 9 Putting Logic to Work: Control of Reasoning ::: 10 Assumption-Based Truth Maintenance ::: 11 Nonmonotonic Reasoning ::: 12 Probability ::: 13 Putting Knowledge to Work: Frames and Semantic Nets ::: 14 Planning ::: 15 Learning ::: 16 Vision ::: 17 Nature Language ::: 18 Expert Systems ::: 19 Concluding Remarks <s> BIB024 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Abstract A new computational framework is presented, called agent-oriented programming (AOP), which can be viewed as a specialization of object-oriented programming . The state of an agent consists of components such as beliefs, decisions, capabilities, and obligations; for this reason the state of an agent is called its mental state . The mental state of agents is described formally in an extension of standard epistemic logics: beside temporalizing the knowledge and belief operators, AOP introduces operators for obligation, decision, and capability. Agents are controlled by agent programs , which include primitives for communicating with other agents. In the spirit of speech act theory , each communication primitive is of a certain type: informing, requesting, offering, and so on. This article presents the concept of AOP, discusses the concept of mental state and its formal underpinning, defines a class of agent interpreters, and then describes in detail a specific interpreter that has been implemented. <s> BIB025 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Part 1 Language issues: abstraction and modularity mechanisms for concurrent computing, Gul Agha et al trade-offs between reasoning and modelling, Peter Wegner a survey of logic programming-based object-oriented languages, Andrew Davison analysis of inheritance anomaly in object-oriented concurrent programming languages, Satoshi Matsuoka and Akinori Yonezawa composing active objects, Oscar Nierstrasz. Part 2 Programming constructs: supporting modularity in highly-parallel programs, Andrew A. Chien multiple-concurrency control policies in an object-oriented programming system, Gail E. Kaiser et al ports for objects in concurrent logic programs, Sverker Janson et al. Part 3 Language design: specifying concurrent languages and systems with "delta"-grammars, Simon M. Kaplan et al interaction abstract machines, Jean-Marc Andreoli et al CC++ - a declarative concurrent object-oriented programming notation, K. Mani Chandy and Carl Kesselman a logical theory of concurrent objects and its realization in the Maude Language, Jose Meseguer. Part 4 Operating Systems: CHOICES - a parallel object-oriented operating system, Roy H. Campbell and Nayeem Islam COSMOS - an operating system for a fine-grain concurrent computer, Waldemar Horwat et al. Part 5 Performance monitoring: monitoring concurrent object-based programs, Bruce A. Delagiet et al. <s> BIB026 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Oz is an experimental higher-order concurrent constraint programming system under development at DFKI. It combines ideas from logic and concurrent programming in a simple yet expressive language. From logic programming Oz inherits logic variables and logic data structures, which provide for a programming style where partial information about the values of variables is imposed concurrently and incrementally. A novel feature of Oz is that it accommodates higher-order programming without sacrificing that denotation and equality of variables are captured by first-order logic. Another new feature of Oz is constraint communication, a new form of asynchronous communication exploiting logic variables Constraint communication avoids the problems of stream communication, the conventional communication mechanism employed in concurrent logic programming. Constraint, communication can be seen as providing a minimal form of state fully compatible with logic data structures ::: ::: Based on constraint communication and higher-order programming, Oz readily supports a variety of object-oriented programming styles including multiple inheritance. <s> BIB027 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> From the Publisher: ::: Although numerous review articles on planning have appeared over the years, no systematic attempt has been made to collect the major papers in this field into one volume. The goal of this book is to remedy this situation by bringing together in one place a set of readings that can be used to develop a familiarity with the planning literature, with the major AI theory underlying planning, and with the exciting directions of current research. ::: ::: The first section of the book introduces the techniques and terminology of the AI planning community. The second section collects papers describing planning systems developed over the past 30 years. A third section presents research in the area of formal models of planning. Finally, a fourth section collects recent work representative of the field's current direction. The editors, three accomplished researchers, provide introductory material surveying the field of planning as well as introductions to groups of papers placing the work in perspective. ::: ::: A useful reference for AI system developers and researchers, this work is also suitable for use as a text for a planning course or as a supplemental text for a more general AI course. <s> BIB028 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> This paper presents a temporal belief logic called \(\mathcal{L}_{TB}\). In addition to the usual connectives of linear discrete temporal logic, \(\mathcal{L}_{TB}\) contains an indexed set of modal belief connectives, via which it is possible to represent the belief systems of resource-bounded reasoning agents. The applications of \(\mathcal{L}_{TB}\) in general, and its use for representing the dynamic properties of multi-agent AI systems in particular, are discussed in detail. A tableau-based decision procedure for \(\mathcal{L}_{TB}\) is then described, and some examples of its use are presented. The paper concludes with a discussion and future work proposals. <s> BIB029 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> Formal analyses of social action for Distributed A.I. (DAI) have focussed, almost exclusively, on scenarios in which participat- ing agents have a joint intention to act. While such scenarios are significant, there are many examples of artificial and natural social systems in which joint intention not only does not occur, but is not even a practical possibility. This paper proposes that a deep theory of social action should account for the whole spectrum of social ac- tion types within the same framework. It is argued that coherence is an attribute which unifies different types of social action, and is therefore a possible starting point for a deep theory. A discussion and subsequent formalisation of coherence is then presented. This model of coherence is used as the foundation upon which to build a new formalisation of team action. The framework in which these formalisations are presented is a new quantifiedmulti-modal logic. <s> BIB030 </s> Agent Theories, Architectures, and Languages: A Survey <s> Further Reading <s> One objective of distributed artificial intelligence research is to build systems that are capable of cooperative problem solving. To this end, a number of implementation oriented models of cooperative problem solving have been developed. However, mathematical models of social activity have focussed only on limited aspects of the cooperative problem solving process: no mathematical model of the entire process has yet been described. In this paper, we rectify this omission. We present a preliminary model that describes the cooperative problem solving process from recognition of the potential for cooperation through to team action. The model is formalised by representing it as a theory in a quantified multi-modal logic. A key feature of the model is its reliance on the twin notions of commitments and conventions; conventions (protocols for monitoring commitments) are formalised for the first time in this paper. We comment on the generality of the model, outline its deficiencies, and suggest some possible refinements and other future areas of research. <s> BIB031
For a detailed discussion of intentionality and the intentional stance, see [34, BIB011 . A number of papers on AI treatments of agency may be found in BIB028 . For an introduction to modal logic, see  ; a slightly older, though more wide ranging introduction, may be found in  . As for the use of modal logics to model belief, see BIB018 , which includes complexity results and proof procedures. Related work on modelling knowledge has been done by the distributed systems community, who give the worlds in possible worlds semantics a precise interpretation; for an introduction and further references, see  BIB019 . Overviews of formalisms for modelling belief and knowledge may be found in  BIB002   . A variant on the possible worlds framework, called the recursive modelling method, is described in BIB021 ; a deep theory of belief may be found in  . Situation semantics, developed in the early 1980s and recently the subject of renewed interest, represent a fundamentally new approach to modelling the world and cognitive systems  BIB014 . However, situation semantics are not (yet) in the mainstream of (D)AI, and it is not obvious what impact the paradigm will ultimately have. Logics which integrate time with mental states are discussed in BIB006 BIB009 BIB029 ; the last of these presents a tableau-based proof method for a temporal belief logic. Two other important references for temporal aspects are BIB003 BIB010 . Thomas has developed some logics for representing agent theories as part of her framework for agent programming languages; see BIB015 BIB022 and section 4. For an introduction to the temporal logics and related topics, see  BIB012 . A non-formal discussion of intention may be found in  , or more briefly  . Further work on modelling intention may be found in BIB007   BIB023 . Related work, focussing less on single-agent attitudes, and more on social aspects, is  BIB030 BIB031 . Until now, this article has been concerned with agent theory -the construction of formalisms for reasoning about agents, and the properties of agents expressed in such formalisms. Our aim in this section is to shift the emphasis from theory to practice. We consider the issues surrounding the construction of computer systems that satisfy the properties specified by agent theorists. We begin by looking at the symbolic AI paradigm, and the assumptions that underpin it. Most introductory textbooks on AI discuss the physical symbol system hypothesis; a good recent example of such a text is BIB024 . There are many objections to the symbolic AI paradigm, in addition to those we have outlined above. Again, introductory textbooks provide the stock criticisms and replies. There is a wealth of material on planning and planning agents. See [51] for an overview of the state of the art in planning (as it was in 1987), BIB028 for a thorough collection of papers on planning, (many of the papers cited above are included), and BIB008 for a detailed description of SIPE, a sophisticated planning system used in a real-world application (the control of a brewery!) Another important collection of planning papers is  . The books by Dean & Wellman and Allen et al. contain much useful related material BIB016 BIB017 . There is now a regular international conference on planning; the proceedings of the first were published as  . The collection of papers edited by Maes  contains many interesting papers on alternatives to the symbolic AI paradigm. Kaelbling  presents a clear discussion of the issues associated with developing resource-bounded rational agents, and proposes an agent architecture somewhat similar to that developed by Brooks. A proposal by Nilsson for teleo reactive programs -goal directed programs that nevertheless respond to their environment -is described in  . The proposal draws heavily on the situated automata paradigm; other work based on this paradigm is described in BIB025 BIB020 . Schoppers has proposed compiling plans in advance, using traditional planning techniques, in order to develop universal plans, which are essentially decision trees that can be used to efficiently determine an appropriate action in any situation BIB004 . Other proposals for 'reactive planners' are reactive action packages BIB005 and competence modules BIB013 . A hybrid architecture specifically developed for multi-agent applications is described in  . There are many other languages which, while they may not be agent languages in the sense we have described, are nevertheless of interest. Concurrent object languages are of considerable interest in software engineering. The notion of a self-contained concurrently executing object, with some internal state that is not directly accessible to the outside world, responding to messages from other such objects, is very close to the concept of an agent as we have defined it. The only significant difference is that our agents are defined in terms of beliefs, goals, and so on. The earliest concurrent object framework was Hewitt's Actor model BIB001  ; another well-known example is the ABCL system  . A recent collection of papers on concurrent object systems is BIB026 . Other languages of interest include Oz BIB027 and IC PROLOG II  . The latter, as its name suggests, is an extension of PROLOG, which includes multiple-threads, high-level communication primitives, and some object-oriented features.