UNIK Multiagent systems Lecture 3. Communication. Jonas Moen

Transcription

1 UNIK Multiagent systems Lecture 3 Communication Jonas Moen

2 Highlights lecture 3 Communication* Communication fundamentals Reproducing data vs. conveying meaning Ontology and knowledgebase Speech acts FIPA and JADE as tools for agent-based communication *Wooldridge, 2009: chapter 6 and

3 Communication Communication = the process of shearing data/information/meaning A topic of central importance in computer science for a long time in parallel, distributed, and agent-based systems

4 Communication How can agents understand each other? The area of 1. Communicating data 2. Ontologies for meaning

5 Communication fundamentals Claude Shannon The father of information theory Shannon founded information theory with A Mathematical Theory of Communication, published in Image: Wikipedia

6 Communication fundamentals Communication, in terms of Shannon, is not concerned by meaning, but the reproduction of bit-strings from one place to another in a noisy environment. We talk about 1. Bits per second transferred 2. Probability of error per bit transferred i.e. we do not talk about the meaning of the bit-string

7 Communication fundamentals Power [W] Time [s] EEnnnnnnnnnn = PPPP dddd Frequency [s -1 ]

8 Communication fundamentals Communication system Communication system «Hello» «Hallo» Image: Wikipedia

9 Communication fundamentals PPPPPP = Pulse Code Modulation EE bb /NN 0 = Energy per bit to the noise spectral density PP ee = Probability of symbol error Communication, in terms of Shannon, is a compromise: Signal-to-Noise Rate (SNR), bit-rate and bit error-rate Image: Figure 4.6, Haykin 2001, Communication systems

10 Example Let us calculate SSSSSS and PP ee if distance between two radios is halved: (1) SNR 0 = EE 0bb = 1 PP 0 tt 0 NN 0 4ππrr2 0 NN 0 (2) SNR 1 = 1 PP 0 tt 0 4ππrr2 1 NN 0 If we put (1) into (2) we get Image: Figure 4.6, Haykin 2001, Communication systems

11 Example Giving PP 0 tt 0 NN 0 = 4ππrr 0 2 SNR 0 SNR 1 = 1 PP 0 tt 0 4ππrr2 1 NN 0 SNR 1 = 1 4ππrr 1 2 4ππrr 0 2 SNR 0 SNR 1 = rr 0 rr 1 2 SNR 0 Image: Figure 4.6, Haykin 2001, Communication systems

12 Example So if rr 0 = 100 m, SSSSSS 0 = 5 db SNR 1 = SNR 1 = = 11 db Using the relation for decibel yy dddd = 10 log 10 xx[ww] yy [dddd] xx WW = Image: Figure 4.6, Haykin 2001, Communication systems

13 Example So by reducing the distance in two SSSSSS increases by 4 and the error rate PP ee ~10 2 ~10 7. A big difference! Image: Figure 4.6, Haykin 2001, Communication systems

14 Communication fundamentals How do we get a binary string from node NN ii to node NN jj as efficiently as possible? NN ii NN ll NN kk NN jj

15 Communication fundamentals How do we get a binary string from node NN ii to node NN jj as efficiently as possible? NN ii NN ll Direct communication between NN ii and NN jj could give low bit-rates due to distance. NN kk NN jj

16 Communication fundamentals How do we get a binary string from node NN ii to node NN jj as efficiently as possible? NN ii NN ll Ad hoc networking via node NN kk could boost bit-rates. NN kk NN jj

17 Communication fundamentals How do we get a binary string from node NN ii to node NN jj as efficiently as possible? NN ii NN ll If node NN kk is unavailable node NN ll could be used instead. NN kk NN jj

18 Communication fundamentals How do we get a binary string from node NN ii to node NN jj as efficiently as possible? NN ii NN ll What if the position of all the nodes are dynamic? NN kk NN jj

19 Communication fundamentals How do we get a binary string from node NN ii to node NN jj as efficiently as possible? NN ii NN ll And what if node NN kk simultaneously wants to talk to node NN ll? NN kk NN jj

20 Communication fundamentals 1. Direct communication between NN ii and NN jj could give low bit-rates due to distance. 2. Ad hoc networking via node NN kk could boost bit-rates 3. If node NN kk is unavailable node NN ll could be used instead 4. What if the position of all the nodes are dynamic and they cross-talk? Network protocol for routing is part of communication and very important in real multiagent systems

21 Ontology An ontology is a specification of a terminology (a set of terms) intended to provide a common basis of understanding about some domain

22 Ontology «An ontology is a formal definition of a body of knowledge. The most typical type of ontology used in building agents involves a structural component. Essentially a taxonomy of classes and subclass relations coupled with definitions of the relationships between these things», [Hendler]

23 Ontology In terms of computer science: The development and deployment of ontologies have their origins in the semantic web research The semantic web is the main motivation for research into ontologies

24 Ontology fundamentals Ontologies describe relations between objects/concepts (as in object-oriented design) Image: Figure 6.1, Wooldridge

25 Ontology fundamentals Defining new terms in relation to old ones Classes and instances Class properties Subclasses, superclasses and inheritance

26 Ontology fundamentals Ontology vs. knowledgebase 1. Ontology is the structural part of a class diagram 2. Knowledgebase is an ontology together with a set of instances of classes

27 The ontology spectrum Depending on the use of an ontology Image: Figure 6.2, Wooldridge

28 The ontology spectrum Informal ontology: 1. Controlled vocabulary - Keywords 2. Terms and glossary - Keywords with loose explanation 3. Thesaurus - Keywords with synonyms for matching 4. Informal is-a taxonomy Classification based on informal clustering. (e.g. cameras and camera bags at Amazon)

29 The ontology spectrum Formal ontology: 5. Formal is-a taxonomy Explicit definition of subsumption relation between classes. 6. Properties - Define class properties 7. Value restrictions (e.g. one mother) 8. Arbitrary logical constraints May result in complex reasoning

30 The ontology hierarchy Image: Figure 6.3, Wooldridge

31 The ontology hierarchy 1. Upper ontology Most general kind of classes imaginable (living or nonliving) thing. 2. Domain ontology General ontology in a particular domain. Reuse is important for coherence between agents. 3. Application ontology 1 and 2 applied to the specific problem case under study. Often not reusable

32 Ontology languages 1. XML - The Extended Markup Language, [XML, 2008] a) Ad hoc ontologies with controlled vocabulary b) XML extends HTML 2. OWL The Web Ontology Language, [Bechhofer, 2004] a) A collection of several XML-based frameworks b) OWL-lite, basic ontologies c) OWL-DL, description logic fomulation d) OWL-full, with consistency check and reasoning

33 Ontology languages 3. KIF Knowledge Interchanging Format [Genersereth and Fikes, 1992] Ontologies in first-order logic 4. RDF Resource Definition Framework a) Not a proper ontology. The goal of RDF is to provide a standardized knowledge representation framework for the web. b) Very simple and effective, at least compared to OWL c) Triplets of subject-predicate-object e.g. ismarriedto(michael, Janine) a knowledgebase

34 Constructing an ontology Method proposed by [Noy and McGuinness, 2004] Image: Figure 6.8, Wooldridge

35 Constructing an ontology 1. Define domain and scope E.g. requirements in software design 2. Consider reuse Sharing meaning is important, use existing ontologies? 3. Enumerate all the relevant terms List important words, do not organize yet 4. Define classes and class hierarchy Top-down or bottom-up, avoid trivial classes. Focus on meaning and general relations

36 Constructing an ontology 5. Define properties a) Intrinsic properties - natural properties like weight b) Extrinsic properties - attached properties like name c) Identify components of an object d) Identify other relationships

37 Constructing an ontology 6. Define properties of properties a) Cardinality constraints b) Type constraints c) Range constraints d) Domain constraints 7. Create instances Find most specific class to represent object

38 Software tools for ontologies 1. Ontolingua server [Farquhar et al., 1997] 2. Protege [Protege Group, 2004] 3. NEON Toolkit [NEON project, 2008 ] Use common sense when constructing an ontology and reuse ontologies for efficient communication

39 Communication Many formalisms exist for communication in concurrent systems: 1. Synchronization - type lost update. 2. Object-oriented programming External objects invoke/execute methods in other objects 3. Agent-based setting Agents determine on their own to act or not based on information. The sender must try to manipulate desires and beliefs in receiver in order to obtain desired action

40 Speech acts Speech act theory treats communication as actions that alter the mental state of the communication participants

41 Speech acts Philosopher John Austin [Austin, 1962] noted that a certain class of utterances had the characteristics of actions, in the sense of trying to change the state of the world, analogous to physical actions. Austin identified a number of performative verbs, like request, inform and promise

42 Speech acts Searl [Searl, 1969] extended Austin 1. Identified several properties for a successful speech act: a) Normal I/O conditions, i.e. good link b) Preparatory conditions, i.e. it must be possible for speaker to speech and speaker must believe that hearer is able to act. c) Sincerity condition. i.e. the speaker must want action of hearer

43 Speech acts Searl [Searl, 1969] extended Austin 2. Types of speech acts a) Representative speak the truth of an expressed proposition, i.e. informing b) Directive attempt to get hearer to do something, i.e. requesting c) Commissives commit the speaker to a course of action, i.e. promise d) Expressive express some psychological state, like gratitude e) Declaration effect some change in an institutional state of affair, like declare war

44 Speech acts The plan-based theory of speech acts [Cohen and Perrault, 1979] How pre-conditions and post-conditions of speech acts could be represented as operators describing desires and beliefs of participants of speech acts. i.e. a formalism for reasoning with speech acts for actions

45 Speech acts Speech acts as rational action [Cohen and Levesque, 1990] A more general connection to rational action

46 Agent communication languages KSE Knowledge Sharing Effort [DARPA, 1990s] Generated two main deliverables 1. KQML - Knowledge Query and Manipulation Language Message-based language for agent communication. Not concerned about content. 2. KIF Knowledge Interchanging Format The content part (domain specific) of KQML

47 Agent communication languages FIPA the Foundation of Intelligent Physical Agents [FIPA, 1999] 1. IEEE standardization for agent systems: 2. The ACL Agent Communication Language 3. Conformance testing. Does a particular agent program respect the semantics of the language?

48 Agent communication languages JADE Java Agent Development Environment [Bellifemine et al., 2007] 1. An infrastructure for deploying FIPA agent systems. 2. De facto standard in agent-based communication on the net

49 Summary of lecture 3* Communication fundamentals (reproducing data) The physics of RF communication SNR, bit-rate and bit error-rate Ontologies and knowledgebases? (conveying meaning) Speech acts (manipulation) FIPA and JADE as tools for agent-based communication ROS as a framework for autonomous mobile robots (Aleksander Simonsen will introduce ROS after next lecture) *Wooldridge, 2009: chapter 6 and