DOCSLIB.ORG

Programming the Deliberation Cycle of Cognitive Robots

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Programming the Deliberation Cycle of Cognitive Robots From: AAAI Technical Report WS-02-05. Compilation copyright © 2002, AAAI (www.aaai.org). All rights reserved. Programming the Deliberation Cycle of Cognitive Robots Mehdi Dastani¤ and Frank de Boer and Frank Dignum and Wiebe van der Hoek and Meindert Kroese and John-Jules Meyer Institute of Information and Computing Sciences Utrecht University P.O.Box 80.089 3508 TB Utrecht The Netherlands Abstract incorporated in the robot’s goals. But, we believe that doing so violates the basic programming principle called separa- This paper presents an overview of ongoing research that aims to develop a programming language for high level con- tion of concerns. Therefore, we aim to distinguish these two trol of cognitive robots and software agents. The language is levels explicitly and develop programming constructs to im- called 3APL and its formal specification is already presented plement these levels separately. in (Hindriks et al. 1999). We explain 3APL programming In this paper, we present our ongoing research on develop- constructs and its existing interpreter. We argue that a part ing a programming language for cognitive robots. The pro- of the deliberation cycle, which is fixed in the interpreter, gramming language is called 3APL (Hindriks et al. 1999) should be programmable. We discuss a set of programming and it is designed to implement the deliberative behavior of constructs that can be used to program some aspects of the cognitive robots. At this stage, the 3APL programming lan- deliberation cycle. guage provides a set of programming constructs to imple- ment robots’ mental attitudes such as its beliefs, goals, basic Introduction actions, and revision rules. The interaction between these For many realistic applications, a cognitive robot should entities are not programmable yet and is fixed by the 3APL have both reactive and deliberative behavior (Fischer, interpreter. This interpreter determines the flow of control Muller,¨ & Pischel 1994; Konolige 1997; Shanahan 1999; through a so-called deliberation cycle. In each cycle, a sub- Giacomo, Lesperance,´ & Levesque 2000). The first type of set of goals is revised, selected and executed. As the delib- behavior concerns the recognition of emergency situations in eration cycle is fixed by the interpreter, the meta-level con- time and provides rapid responses whereas the second type cerns can only be implemented to the extent that they are of behavior concerns the planning of actions to achieve its implementable indirectly by the set of object-level program- long term goals. In order to implement a robot’s deliberative ming constructs and given that the programmers know how behavior, its mental attitudes such as goals and beliefs as the flow of control is implemented in the 3APL interpreter. well as the interaction between these attitudes should be im- The aim of our research is to extend the 3APL language with plemented. Issues related to the implementation of robots’ a second set of programming constructs by means of which mental attitudes can be considered as object-level concerns the deliberation cycle becomes programmable. This exten- while issues related to the implementation of the interaction sion should make 3APL a programming language for cogni- between mental attitudes form meta-level concerns. tive robots that respects the separation of concerns principle. To illustrate these levels, consider a cognitive mobile This paper is organized as follows. First, we present the robot at rest. The goal of this robot is to transport boxes from architecture of cognitive robots that can be programmed by a source position to a different position, whenever there are 3APL. The syntax and semantics of the 3APL program- boxes at the source position. Another goal of the robot is ming language is explained. An example of a 3APL pro- to clean a room whenever it is not clean. The robot can gram is proposed to implement the above mobile robot sce- achieve these goals in several ways. For example, the robot nario. Subsequently, we explain the deliberation cycle of can transport boxes before cleaning the room, or vice versa, the 3APL interpreter and discuss the needs for programming or transport one box and clean the room for a certain amount constructs to implement the deliberation cycle. Finally, we of time and repeat it again. The beliefs and goals of the robot indicate some future research directions and conclude the form the object-level concerns while the strategy of how to paper. achieve these goals is a meta-level concern. Of course, a robot strategy can be considered as an issue that should be An Architecture for Cognitive Robots The cognitive robot or software agent that we consider has ¤Contact author is Mehdi Dastani: email: [email protected]. More information on 3APL can be found at: a mental state consisting of beliefs, goals, basic actions, a http://www.cs.uu.nl/3apl/ set of practical reasoning rules for revising goals, and an Copyright °c 2002, American Association for Artificial Intelli- interpreter. The beliefs represent the robot’s general world gence (www.aaai.org). All rights reserved. knowledge as well as its knowledge about the surrounding environment. In contrast to the BDI tradition (Cohen & PRACTICAL REASONING RULES. Each of these mod- Levesque 1990; Rao & Georgeff 1991), where goals are ules consists of its corresponding programming constructs. considered as states to be reached by the robot, and like Below is an overview of the programming constructs for ConGolog (Giacomo, Lesperance,´ & Levesque 2000), the each module. goals are considered here to be the robot’s control program Definition 1 (Programming Constructs for Beliefs) that specifies its activities. The basic actions are the actions Given a set of domain variables and functions, the set of that the robot can perform. These actions may be cogni- domain terms is defined as usual. Let t1; : : : ; tn be terms tive actions such as belief updates or external actions such referring to domain elements and P red be a set of domain as moving in a certain direction. In general, a basic action predicates, then the set of programming constructs for belief can be considered as a wrapper that provides a parameter- formula, BF , is defined as follows: ized interface to other processes. The set of practical rea- soning rules contains rules that can be applied to revise ac- ² p(t1; : : : ; tn) 2 BF . tions that are blocked, goals that are not achievable, to op- ² if Á; Ã 2 BF , then :Á; Á ^ Ã 2 BF . timize goals, or to generate some sort of reactive behavior. All variables in the BF formula are universally quantified Finally, the interpreter determines the flow of control among with maximum scope. The belief base module of a cognitive the abovementioned ingredients. The interpreter is usually robot is the following programming construct: specified by a deliberation cycle (Georgeff & Lansky 1987; BELIEF BASE= fÁ j Á 2 BF g. Giacomo, Lesperance,´ & Levesque 2000). This cognitive The set of basic actions is a set of (parameterized) actions architecture, which we call the 3APL architecture, is illus- that can be executed if certain preconditions hold. After ex- trated in Figure 1. ecution of an action certain postconditions must hold. These actions can be, for example, motor actions or belief update operations. Beliefs PR-rules Definition 2 (Programming Constructs for Basic Actions) Let ®(X) be an action name with parameters X and Á; Ã 2 BF . Then, programming constructs for basic Sense Action actions have the form: fÁg ®(X) fÃg Interpreter The basic action module of a cognitive robot is the following programming construct: BASIC ACTIONS= f C j C is a basic action g. Goals BasicActions The set of goal programming constructs that we use here differs from its specification (Hindriks et al. 1999) in some aspects. At this stage, we do not propose programming con- structs for the choice and parallel operators. The absence Figure 1: The 3APL architecture. of the choice operator implies that 3APL goals are deter- ministic programs and the absence of the parallel operator Although the 3APL architecture has many similarities implies that no two activities can take place simultaneously. with other cognitive architectures such as PRS, proposed for However, two new programming constructs are specified, the BDI agents (Rao & Georgeff 1995; Georgeff & Lansky i.e. IF-THEN-ELSE and WHILE-DO. Note that these pro- 1987), they differ from each other in many ways. For exam- gramming constructs are specific uses of the choice operator ple, the PRS architecture is designed to plan agents’ goals and recursion, respectively. (desires) while the 3APL architecture is designed to con- Definition 3 (Goal Programming constructs) Let BA be trol and revise robots’ control program. In fact, a goal in a set of basic actions, BF be a set of belief sentences, the 3APL architecture is a control program which can be ¼; ¼1; : : : ; ¼m 2 GOAL and Á 2 BF . Then, the set of compared to plans in the PRS architecture. Moreover, there programming constructs for 3APL goals (GOAL) can be is no ingredient in the PRS architecture that corresponds to defined as follows: the practical reasoning rules which is a powerful mechanism ² BactionGoal: BA ⊆ GOAL to revise mental attitudes. Finally, the deliberation cycle in ² PredGoal: BF ⊆ GOAL 3APL will become a programmable component while the deliberation cycle in PRS is integrated in the interpreter. ² TestGoal: if Á 2 BF , then Á? 2 GOAL ² SkipGoal: skip 2 GOAL 3APL Programming Language ² SequenceGoal: if ¼1; : : : ; ¼n 2 GOAL, then ¼ ; ... ; ¼ 2 GOAL 3APL (Hindriks et al. 1999) consists of languages for 1 n ² IfGoal: IF Á THEN ¼ ELSE ¼ 2 GOAL beliefs, basic actions, goals, and practical reasoning rules. 1 2 A set of programming constructs has been introduced ² WhileGoal: WHILE Á DO ¼ 2 GOAL. for each of these languages.
Load more

Recommended publications
  • BAAC: a Prolog System for Action Description and Agents Coordination∗
    BAAC: A Prolog System for Action Description and Agents Coordination∗ Agostino Dovier1, Andrea Formisano2, and Enrico Pontelli3 1 Univ. di Udine, Dip. di Matematica e Informatica. [email protected] 2 Univ. di Perugia, Dip. di Matematica e Informatica. [email protected] 3 New Mexico State University, Dept. Computer Science. [email protected] Abstract The paper presents a system for knowledge representation and coordination, where autonomous agents reason and act in a shared environment. Agents autonomously pursue individual goals, but can interact through a shared knowledge repository. In their interactions, agents deal with problems of synchronization and concurrency, and have to realize coordination by developing proper strategies in order to ensure a consistent global execution of their autonomously derived plans. This kind of knowledge is modeled using an extension of the action description language B. A distributed planning problem is formalized by providing a number of declarative specifications of the portion of the problem pertaining a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination platform, implemented in Prolog, is easily modifiable and extensible. New user-defined interaction protocols can be integrated. 1998 ACM Subject Classification I.2.3 Deduction and Theorem Proving, I.2.11 Distributed Artificial Intelligence Keywords and phrases Knowledge Representation, Multi-Agent Systems, Planning, CLP Digital Object Identifier 10.4230/LIPIcs.ICLP.2011.187 1 Introduction Representing and reasoning in multi-agent domains are two of the most active research areas in multi-agent system (MAS) research. The literature in this area is extensive, and it provides a plethora of logics for representing and reasoning about various aspects of MAS, e.g., [13, 9, 17, 15, 7].
    [Show full text]
  • Observation-Based Model for BDI-Agents∗
    Observation-based Model for BDI-Agents∗ Kaile Su1,2, Abdul Sattar1, Kewen Wang1, Xiangyu Luo2, Guido Governatori3, Vineet Padmanabhan3 1Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia 2Department of Computer Science, Sun Yat-sen University, Guangzhou, P.R.China 3School of ITEE, The University of Queensland, Brisbane, Australia {k.su, a.sattar, k.wang}@griffith.edu.au; xiang yu [email protected]; {guido,vnair}@itee.uq.edu.au Abstract the states of computer processes, to S5 epistemic logic. The salient point of the interpreted system model is that we can We present a new computational model of BDI-agents, called get an interpreted system from a computer program, and for- the observation-based BDI-model. The key point of this BDI- model is to express agents’ beliefs, desires and intentions as mulas in epistemic logic that are valid with respect to the in- a set of runs (computing paths), which is exactly a system in terpreted system can be understood as properties of program the interpreted system model, a well-known agent model due computations. In this sense, the interpreted system model is to Halpern and his colleagues. Our BDI-model is computa- computationally grounded (Wooldridge 2000). Thus, it is in- tionally grounded in that we are able to associate the BDI- teresting to extend the interpreted system model to capture agent model with a computer program, and formulas, involv- an agent’s belief, desire and intention as well. ing agents’ beliefs, desires (goals) and intentions, can be un- The aim of this paper is to present a computationally derstood as properties of program computations.
    [Show full text]
  • JAPL: the JADE Agent Programming Language
    272 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 3, AUGUST 2013 JAPL: the JADE Agent Programming Language Mohamed BAHAJ, Abdellatif SOKLABI FSTS/ Department of Mathematics and Computer Science, University Hassan I Settat, Morocco [email protected] [email protected] Abstract— This article describes JADE Agent Programming particular, we highlight knowledge representation. The Language (JAPL) that allows fast and efficient following section contains the programming treatment implementation of intelligent behaviors into mobile agents, agent’s behaviours. The sixth section finalizes this part based on three logical, FIPA speech acts, and a part of with a focus on the service concept, and the last section complex procedural script for actions. It integrates the we wrap up with some conclusions. ontologies and defines communication services. Rather than rely on a library of plans, JAPL allows agents to plan from II. JADE AGENT PROGRAMMING LANGUAGE first principles. It also describes how to program the multiple OVERVIEW: JADE behaviors using JAPL instructions and how to JAPL is the extension of ADL (Action Description compile JAPL to JAVA classes. Language) [18] on the mobile agents of JADE. ADL Keywords— Mobile agents, JADE, Agent programming allows the use of quantifiers and conditional expressions language, agents communication, MAS in the description of operators to describe classical planning. In ADL, the focus of the quantifiers is over I. INTRODUCTION: finite domains and disjunction that are not allowed in the JADE (Java Agent Development Environment) is the expression of effects, because it would bring non- system for mobile agents, most used in the world. It is deterministic actions.
    [Show full text]
  • Acting Within BDI Agent Architecture –
    Automated Planning and Acting – Acting within BDI Agent Architecture – O. Boissier IMT Mines Saint-Etienne, France Défi IA — Spring 2021 Acting I How to perform chosen actions I Acting6=Execution ActorAgent Deliberation components I Agent is situated in a dynamic Objectives Planning unpredictable environment QueriesDeliberation Other Plans actorsagents I Adapt actions to current Actingcomponents Messages context I React to events Commands Percepts I Relies on ExecutionExecution platformplatform I Operational models of actions Actuations Signals I Observations of current state External World I Acting6=Planning 2 Outline Agent Oriented Programming BDI Architecture Agent Oriented Programming with Jason Comparison with other paradigms Conclusions and wrap-up 3 Agent Oriented Programming Features I Reacting to events × long-term goals I Course of actions depends on circumstance I Plan failure (dynamic environments) I Social ability ; see course on Integrating and engineering intelligent systems I Combination of theoretical and practical reasoning I Theoretical reasoning is directed towards beliefs, knowledge ; see course on Knowledge representation and reasoning I Practical reasoning is directed towards actions ; this course 4 Agent Oriented Programming Fundamentals I Use of mentalistic notions and a societal view of computation [Shoham, 1993] I Heavily influenced by the BDI architecture and reactive planning systems [Bratman et al., 1988] 5 Literature Agent Oriented Programming Books: [Bordini et al., 2005], [Bordini et al., 2009] Proceedings: ProMAS, DALT, LADS, EMAS, AGERE, ... Surveys: [Bordini et al., 2006], [Fisher et al., 2007] ... Languages of historical importance: Agent0 [Shoham, 1993], AgentSpeak(L) [Rao, 1996], MetateM [Fisher, 2005], 3APL [Hindriks et al., 1997], Golog [Giacomo et al., 2000] Other prominent languages: Jason [Bordini et al., 2007a], Jadex [Pokahr et al., 2005], 2APL [Dastani, 2008], GOAL [Hindriks, 2009], JACK [Winikoff, 2005], JIAC, AgentFactory But many other languages and platforms..
    [Show full text]
  • 3APL-M Programming Guide
    3APL-M Platform for Lightweight Deliberative Agents Version: 1.4 (Feb-2005) Fernando Koch † http://www.cs.uu.nl/3apl-m Table of Contents Table of Contents ................................................................................................................................. 1 What-is 3APL-M?................................................................................................................................ 2 What-is 3APL?..................................................................................................................................... 2 Software Requirements ........................................................................................................................ 3 Getting started ...................................................................................................................................... 3 For J2SE........................................................................................................................................... 3 For J2ME.......................................................................................................................................... 4 The 3APL-M architecture .................................................................................................................... 6 Programming in 3APL ......................................................................................................................... 7 The Deliberation Cycle .......................................................................................................................
    [Show full text]
  • Architecture for Agent Programming Languages
    Architecture for Agent Programming Languages ¾ ¿ Koen Hindriks½ and Mark d’Inverno and Michael Luck Abstract. eral properties of agents. Equally, it has enabled a consideration of As the field of agent-based systems continues to expand rapidly, the needs for a set of building blocks for agent architectures by spec- one of the most significant problems lies in being able to compare and ifying schemas and actions for the updates of beliefs, goals (decision evaluate the relative benefits and disadvantages of different systems. making), planning and reflection. In this paper we use the formal In part, this is due to the various different ways in which these sys- specification of 3APL as well as components from AgentSpeak(L) tems are presented. One solution is to develop a set of architectural and dMARS specifications to compare and highlight the major dis- building blocks that can be used as a basis for further construction tinctions between them. (to avoid re-inventing wheels), and to ensure a strong and effective, The difficulty in comparing different agent-based approaches in yet simple and accessible, means of presentation that allows for com- part derives from the fact that there is no clear or formal presenta- parison and analysis of agent systems. In this paper, we address this tion of the language or architecture in question. Even when systems issue in providing just such an architectural framework by using the are presented by means of formal semantics, the differences in the 3APL agent programming language as a starting point for identifica- styles and formalisms used do not readily facilitate such comparison.
    [Show full text]
  • A Formal Embedding of Agentspeak L in 3APL 1 Introduction
    AFormal Emb edding of AgentSp eakL in 3APL Ko en Hindriks, Frank S. de Bo er, Wieb e van der Ho ek and John-Jules Ch. Meyer University Utrecht, Department of Computer Science P.O. Box 80.089, 3508 TB Utrecht, The Netherlands fko enh,frankb,wieb e,[email protected] Abstract Agent-based computing in Arti cial Intelligence has given rise to a numb er of diverse and comp eting prop osals for agent programming languages. Agents, in the sense we are using it, are complex mental entities consisting of b eliefs, goals, and intentions. For several reasons it has b een dicult to evaluate and compare the di erent prop osals for agent languages. One of the main reasons, in our opinion, is the lack of a general semantic framework which provides a suitable basis for language comparison. Our aim is to make as much use as p ossible of formal metho ds from the area of programming semantics. In this pap er, we give a formal emb edding of the agent language AgentSp eakL in our own agent language 3APL. To this end we de ne a notion of simulation based on the formal op erational semantics of the languages. The main result of the pap er is a pro of that 3APL can simulate AgentSp eakL. As a consequence, 3APL has at least the same expressive power as AgentSp eakL. The comparison yields some new insights into the features of the agent languages. One of the results is that AgentSp eakL can b e substantially simpli ed.
    [Show full text]
  • Architecture for Agent Programming Languages
    Architecture for Agent Programming Languages ¾ ¿ Koen Hindriks ½ and Mark d’Inverno and Michael Luck Abstract. eral properties of agents. Equally, it has enabled a consideration of As the field of agent-based systems continues to expand rapidly, the needs for a set of building blocks for agent architectures by spec- one of the most significant problems lies in being able to compare and ifying schemas and actions for the updates of beliefs, goals (decision evaluate the relative benefits and disadvantages of different systems. making), planning and reflection. In this paper we use the formal In part, this is due to the various different ways in which these sys- specification of 3APL as well as components from AgentSpeak(L) tems are presented. One solution is to develop a set of architectural and dMARS specifications to compare and highlight the major dis- building blocks that can be used as a basis for further construction tinctions between them. (to avoid re-inventing wheels), and to ensure a strong and effective, The difficulty in comparing different agent-based approaches in yet simple and accessible, means of presentation that allows for com- part derives from the fact that there is no clear or formal presenta- parison and analysis of agent systems. In this paper, we address this tion of the language or architecture in question. Even when systems issue in providing just such an architectural framework by using the are presented by means of formal semantics, the differences in the 3APL agent programming language as a starting point for identifica- styles and formalisms used do not readily facilitate such comparison.
    [Show full text]
  • 3APL Platform
    3APL Platform 2nd October 2003 Masters' thesis Computer Science Utrecht University E.C. ten Hoeve INF/SCR-03-02 Supervisors: dr. M. M. Dastani dr. F.P.M. Dignum Prof. dr. J.-J. Ch. Meyer Abstract The 3APL platform is an experimental multiagent platform, designed to support the FIPA specications. It provides a graphical interface in which a user can develop and execute agents using several tools, such as a syntax-colored editor and several debugging tools. The platform also allows for communication among agents present on the platform as well as other agents hosted on another 3APL platform. This thesis describes the design and implementation of this platform. It also compares several existing platforms, namely JADE,JACK and ZEUS with the 3APL platform on purpose, communication and the phases of traditional software development (analysis, design, implementation, deployment). 3 Acknowledgements Many thanks to dr. Mehdi Dastani for his support, comments and ideas. I would also like to thank dr. Frank Dignum for his input and suggestions. Finally I would like to thank Ir. Meindert Kroese for his assistance in integrating and adapting the existing 3APL software to the agent platform. 5 CONTENTS CONTENTS Contents 1 Introduction 9 2 What is 3APL? 11 2.1 Beliefs . 11 2.2 Actions . 12 2.3 Goals . 12 2.4 Practical reasoning rules . 13 2.5 Semantics of 3APL . 13 2.6 Deliberation cycle . 14 2.7 An example program. 15 3 What is a platform ? 17 3.1 Introduction . 17 3.2 FIPA Standards . 17 3.2.1 AMS . 18 3.2.2 Agent .
    [Show full text]
  • Internalising Interaction Protocols As First-Class Programming Elements in Multi Agent Systems
    Internalising Interaction Protocols as First-Class Programming Elements in Multi Agent Systems by David J. Lillis, B.A., H.Dip., M.Sc. The thesis is submitted to University College Dublin for the degree of Doctor of Philosophy in the College of Science October 2012 arXiv:1711.02634v1 [cs.MA] 7 Nov 2017 School of Computer Science and Informatics Mr. John Dunnion (Head of School) Under the supervision of Dr. Rem W. Collier Mr. John Dunnion CONTENTS Abstract xi Acknowledgements xiv List of Publications xvi 1 Introduction 1 1.1 The Position of Multi Agent Systems . 2 1.2 Conversation Management for Multi Agent Systems . 4 1.3 Motivations . 5 1.4 Core Contributions . 7 1.5 Thesis Outline . 8 I Background 11 2 Agent Oriented Programming 12 2.1 Introduction . 12 2.2 What is an “agent”? . 13 2.2.1 Autonomy . 14 2.2.2 Social Ability/Interoperability . 14 2.2.3 Perception and Environment . 15 2.2.4 Proactivity and Reactivity . 15 2.2.5 Benevolence . 15 2.2.6 Mobility . 16 2.2.7 Other Properties . 16 2.2.8 Adopting a Definition . 17 2.3 Agent Oriented Programming . 18 2.4 AOP Frameworks and Languages . 19 2.4.1 AgentSpeak(L) and Jason . 23 2.4.2 Agent Factory . 25 2.4.3 GOAL . 28 2.4.4 JADE and Jadex . 30 2.4.5 2APL . 34 2.5 Summary . 38 i 3 Agent Communication 39 3.1 Introduction . 39 3.2 Standalone Agent Communication . 39 3.2.1 Speech Act Theory . 40 3.2.2 Criticisms of ACLs .
    [Show full text]
  • Goal-Oriented Modularity in Agent Programming∗
    Goal-Oriented Modularity in Agent Programming∗ M. Birna van Riemsdijk1 Mehdi Dastani1 John-Jules Ch. Meyer1 Frank S. de Boer1,2 Utrecht University1 CWI, Amsterdam2 The Netherlands {birna,mehdi,jj,frankb}@cs.uu.nl ABSTRACT can be changed (or at least the non-public part of a module), Modularization is widely recognized as a central issue in without affecting other modules. software engineering. In this paper we address the issue of In this paper we address the issue of modularization in cog- modularization in cognitive agent programming languages. nitive agent programming languages. Cognitive agents are We discuss existing approaches to modularity in cognitive agents endowed with high-level mental attitudes such as be- agent programming. Then, we propose a new kind of mod- liefs, goals, plans, etc. Several programming languages and ularity, i.e., goal-oriented modularity, which takes the goals platforms have been introduced to program these agents, of an agent as the basis for modularization. Further, we such as 3APL [10, 6], AgentSpeak(L) [14, 3], JACK [20], present a formal semantics of goal-oriented modularity in Jadex [13], etc. Some of these languages incorporate sup- the context of the 3APL agent programming language. port for modularization, which we discuss in some detail in section 2.1. Our contribution is the proposal of a new kind of modular- 1. INTRODUCTION ity, i.e., goal-oriented modularity, which is, as we will argue, Modularization is widely recognized as a central issue in particularly suited for agent programming languages (sec- software engineering [11, 9, 2]. A system which is composed tion 2).
    [Show full text]
  • Towards Verifying GOAL Agents in Isabelle/HOL
    Towards Verifying GOAL Agents in Isabelle/HOL Alexander Birch Jensen a DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kongens Lyngby, Denmark Keywords: Agent Programming, Formal Verification, Proof Assistants. Abstract: The need to ensure reliability of agent systems increases with the applications of multi-agent technology. As we continue to develop tools that make verification more accessible to industrial applications, it becomes an even more critical requirement for the tools themselves to be reliable. We suggest that this reliability ought not be based on empirical evidence such as testing procedures. Instead we propose using an interactive theorem prover to ensure the reliability of the verification process. Our work aims to verify agent systems by emdedding a verification framework in the interactive theorem prover Isabelle/HOL. 1 INTRODUCTION considers related work in the literature. Section 3 introduces the semantics of GOAL and a verifica- A key issue in deployment of multi-agent systems is tion framework for GOAL agents. Section 4 goes to ensure its reliability (Dix et al., 2019). We ob- into details with our work on formalizing GOAL in serve that testing does not translate well from tradi- Isabelle/HOL. Section 5 discusses some of the future tional software to multi-agent technology: we often challenges. Finally, Section 6 makes concluding re- have agents with complex behavior and the number marks. of possible system configurations may be intractable. Two leading paradigms in ensuring reliability are formal verification and testing. The state-of-the-art 2 RELATED WORK approach for formal verification is model-checking (Bordini et al., 2006) where one seeks to verify de- sired properties over a (generalized) finite state space.
    [Show full text]