Impressions of EASSS, Workshops & AAMAS

1. Impressions of EASSS, Workshops & AAMAS Jan Dijkstra

2. EASSSEuropean Agents Systems Summer School • An initiative of AgentLink • AgentLink  Europe’s IST-funded Network of Excellence for Agent-Based Computing • 150 participants

3. Topics EASSS • AgentLink – Michael Luck • Introduction to Agents / Intelligent Agents – Michael Wooldridge • Logical Foundations of Agent-based Systems – o.a. John-Jules Meyer • Agent-bases Simulation of Societies – Nigel Gilbert • Agent Mediated Electronic Commerce – Frank Dignum • Intelligent Information Agents for the Internet – Mathias Klusch • Agent Oriented Software Engineering Methodologies – o.a. Onn Shehory • Agent Autonomy – Henry Hexmoor • Business Applications of Autonomous Agents and Multi-Agent Systems – Jorg Müller • Economically Founded Multiagent Systems – Tuomas Sandholm • Coordination in MAS – Ed Durfee

4. AAMAS • A week-long program that addressed all the key issues in contemporary agent systems research • 16 Workshops • 13 Tutorials • 44 Sessions • Ca 170 paper presentations • Ca 110 poster presentations • 700 delegates

5. Topics AAMAS • Agent Oriented Software Engineering • Bidding and Bargaining Agents • Trust and Reputation • Self-Organizing Systems • Markets And Auctions • Multiagent Simulation • (Mobile) Embodied Agents • Group and Organizational Dynamics • Robot Architectures • Argumentation, Persuasion, and Deception • Agent Communication Languages and Protocols • Formalisms and Logics • Applications in Commerce • Management of Computation

6. AgentLink EASSS • AgentLink coordinates research and development of agent-based computer systems on the behalf of the European Commission • Industrial action • Research coordination • Teaching and training • Special interest groups • Information structure • http://www.AgentLink.org • http://agents.umbc.edu

7. What is an agent? EASSS AgentLink • A good question, and one that receives an inordinate amount of time from within the agent community itself. Rather as was the case in the early days of object-oriented development, there is no one universally accepted definition of an agent, but instead a lot of people working in closely related areas, using similar ideas.

8. What is an agent? EASSS AgentLink • Put crudely, an agent is an autonomous software system: a system that can decide for itself what it needs to do. • Two main “religions” appear: • Intelligent agents community Intelligent agents are agents that can do reasoning or planning. • Mobile agents community Mobile agents are agents that can transmit themselves across a computer network and recommence execution on a remote site.

9. Overview EASSS AAMAS • Five ongoing trends have marked the history of computing • Ubiquity • Interconnection • Intelligence • Delegation • Human-orientation • Programming has progressed through • Sub-routines • Procedures & functions • Abstract data types • Objects to agents. Wooldridge

10. EASSS AAMAS • An agent is a computer system that is capable of independent action on behalf of its user or owner. • A multiagent system is one that consists of a number of agents, which interact with one-another • In order to successfully interact, agents need ability to cooperate, coordinate, and negotiate. Two key problems • How do we build agents that are capable of independent, autonomous action in order to successfully carry out the tasks that we delegate to them? • How do we build agents that are capable of interacting (cooperating, coordinating, negotiating) with other agents in order to successfully carry out the tasks that we delegate to them, particularly when the other agents cannot assumed to share the same interests/goals? Wooldridge

11. What is an agent? EASSS AAMAS • The main point about agents is they are autonomous: capable of acting independently, exhibiting control over their internal state. • Thus: an agent is a computer system capable of autonomous action in some environment. • An intelligent agent is a computer system capable of flexible autonomous action in some environment. By flexible, we mean: • Reactive • Pro-active • Social Wooldridge

12. Other properties EASSS AAMAS • Other properties, sometimes discussed in the context of agency: • Mobility • Veracity • Benevolence • Rationality • Learning / adaptation Wooldridge

13. Agents and Objects EASSS AAMAS • Are agents just objects by another name? • Object: • Encapsulates some state • Communicates via message passing • Has methods, corresponding to operations that may be performed on this state • Main differences • Agents are autonomous • Agents are smart • Agents are active Wooldridge

14. Environments EASSS AAMAS • Accessible vs. inaccessible • Deterministic vs. non-deterministic • Episodic vs. non-episodic • Discrete vs. continuous • Static vs. dynamic Wooldridge

15. Agents as Intentional Systems EASSS AAMAS • Human behavior is often predicted and explained through the attribution of attitudes, such as believing and wanting, hoping, fearing, and so on. • The attitudes employed in such folk psychological descriptions are called the intentional notions. • The term intentional system describes the entities ‘whose behavior can be predicted by the method of attributing belief, desires and rational acumen’.

16. IntentionalStance 1 EASSS AAMAS • Daniel Dennett coined the term intentional system to describe entities ‘whose behavior can be predicted by the method of attributing belief, desires and rational acumen’. • He identifies different ‘grades’ of intentional system: • A first-order intentional system has beliefs and desires but no beliefs and desires about beliefs and desires. • A second-order intentional system is more sophisticated; it has beliefs and desires about beliefs and desires-both those of others and its own.

17. IntentionalStance 2 EASSS AAMAS • Intentional notions are abstraction tools, which provide us with a convenient and familiar way of describing, explaining, and predicting the behavior of complex systems. • Most important developments in computing are based on new abstractions: • Procedural abstraction • Abstract data types • Objects Agents, and agents as intentional systems, represent a further, and increasingly powerful abstraction. • So agent theorists start from the view of agents as intentional systems: one whose simplest consistent description requires the intentional stance.

18. IntentionalStance 3 EASSS AAMAS • This intentional stance is an abstraction tool – a convenient way of talking about complex systems, which allows us to predict and explain their behavior without having to understand how the mechanism actually works. • Now, much of computer science is concerned with looking for abstraction mechanisms • Therefore why not use the intentional stance as an abstraction tool in computing – to explain, understand computer systems?

19. Post-Declarative Systems EASSS AAMAS • In procedural programming, we say exactly what a system should do; • In declarative programming, we state something that we want to achieve, give the system general information about the relationships between objects, and let a built-in control mechanism figure out what to do; • With agents, we give a very abstract specification of the system, and let the control mechanism figure out what to do, knowing that it will act in accordance with some built-in theory of agency.

20. Abstract Architectures for Agents EASSS AAMAS • Assume the environment may be in any of finite set Eof discrete, instantaneous states: E={e,e’,…} • Agents are assumed to have a repertoire of possible actions available to them, which transform the state of the environment: Ac = {α,α’,…} • A run, r, of an agent in an environment is a sequence of interleaved environment states and actions:

21. State Transformer Functions EASSS AAMAS • A state transformer function represents behavior of the environment: • If (r) = , then there are no possible successor states to r. in this case, we say that the system has ended its run. • Formally, we say an environment Env= E,e0, where: E is a set of environment states, e0 E is the initial state; and is a state transformer function.

22. Agents, Systems EASSS AAMAS • Agent is a function which maps runs to actions: Ag :RE  Ac An agent makes a decision about what action to perform based on history of the system that it has witnessed to date. Let AGbe the set of all agents. • A system is pair containing an agent and an environment. any system will have associated with a set of possible runs; the set of runs of agent Ag in environment Envby R (Ag , Env ).

23. Perception EASSS AAMAS • The seefunction is the agent’s ability to observe its environment whereas the action function represents the agent’s decision making process. • Outputof the seefunction is a percept: see : E  Per which maps environment states to percepts, andactionis now a function action: Per* A which maps sequences of percepts to actions. • Next function maps an internal sate and percept to an internal state ; tasks etc.

24. Utilities Functions EASSS AAMAS • One possibility: associate utilities with individual states – the task of the agent is then to bring about states that maximize utility. • The optimal agent Ag in an environment Envis the one thatmaximizes expected utility.

25. Logics for Multiagent Systems EASSS AAMAS • Theorists conceptualize agents. • Different attitudes may be used to characterize agents. • Attitudes can be formalized; modal logic can be used as a tool for reasoning about attitudes, focusing on knowledge/belief. • That gives a semantics to the architectures, languages, and tools – literally, a meaning.

26. Attitudes EASSS AAMAS • Information attitudes • Belief • Knowledge • Pro-attitudes • Desire • Intention • Obligation • Commitment • Choice • …… • Most-studied aspect of practical reasoning agents: interaction between knowledge and action

27. Agent-Oriented Software Engineering EASSS AAMAS EASSS by Onn Shehory and Franco Zambonelli WS organized by James Odell and Gerhard Weiss • The concept of an agent as an autonomous system, capable of interacting with other agents in order to satisfy its design objectives, is a natural one for software designers. This led to a growth of interest in agents as new paradigm for software engineering. • Topics • Methodologies and Tools • Analysis, Design and Requirements Engineering • UML and Agents Systems • Patterns, Architecture en Reuse

28. Key Characteristics of Agents (SE Viewpoint) EASSS AAMAS • Basic • Autonomy & Proactivity • Situatedness • Interactivity • Additional • Mobility & Locality • Openness • Learning & Adaptive Capabilities AOSE

29. Agent Autonomy EASSS AAMAS • Process-based and Object-based applications • Global goal achieved via global control scheme for the application entities • Agent-based applications • Sub-goals assigned to autonomous agents integrating execution capabilities • Implies perceiving agents as proactive entities • Multiple independent loci of control in applications • SE advantages • Control encapsulation as a dimension of modularity AOSE

30. Agent Situatedness EASSS AAMAS • Agents typically perceive a portion of the external world- an ‘environment’ • Physical environment • Computational environment • They have to sense and effect • By perceiving what’s happening in the environment and possibly influencing it • SE advantages • Clear separation of concerns between • The active computation parts of the system (agents) • The resource of the environment AOSE

31. Agent Interactivity EASSS AAMAS • Agents maye execute in multiagent contexts and interact which each other • Agent communication • Agent coordination • Collaborative or competitive interactions • Agents interact to achieve a common goal • Competition as a peculiar form of collaboration • SE implications • Not a single characterizing protocol of interaction AOSE

32. Agent Mobility & Locality EASSS AAMAS • Autonomous components can migrate across different multi-agent systems (or contexts) • Non-Functional Motivations • An be sometimes used to reduce bandwidth (local access to data and services) • Robustness • SE Motivations • Additional dimension of autonomous behavior • Improve locality in interactions AOSE

33. Openess of Multiagent Systems EASSS AAMAS • The agents in a system may not be fixed • New agents can be created or enter a multiagent systems context • Mobile agents can arrive • Technological implications • Need of standards (e.g. FIPA) • SE implications • Controlling self-interested agents AOSE

34. Learning and Adaptive Agents EASSS AAMAS • When agents have to be “intelligent” • They must be possibly able to learn from previous experiences • Improving the effectiveness of their actions • When agents lives in dynamic scenarios • They must be able to adept their behavior to changing situations • Re-shaping themselves • SE is not concerned with • HOW learning and adaptiveness are achieved • But it may be concerned with • WHAT could be the impact on the global software system of having components that change their behavior dynamically? AOSE

35. Agent Oriented Software Engineering Methodologies EASSS AAMAS • KAOS, Tropos, Prometheus, GAIA, AUML, DESIRE, MaSE, ROADMAP, OPM/MAS etc. • Why a new method • Development of agent systems requires a new way of thinking about design • Goals, agents, plans rather than objects & methods AOSE

36. Tropos EASSS AAMAS Two key features • The use of knowledge level concepts , such as agent, goal, plan and other through all phases of software development • A pivotal role assigned to requirements analysis when the environment and the system-to-be is analyzed AOSE

37. Tropos phases EASSS AAMAS • Requirements • Early requirements : identifying roles along with their goals • Late requirements : the system-to-be is introduced as another actor and is related to stakeholder actors in terms of actor dependencies • Architecturaldesign • More system actors are introduced and they are assigned subgoals or subtasks of the goals and tasks assigned to the system • Detailed design • System actors are defined in further detail, including specifications of communication and coordination protocols • Implementation • Specification is transformed into a skeleton for implementation AOSE

38. Tropos concepts and models EASSS AAMAS • Actor : an actor represents a physical agent or a software agent as well as a role or a position • Goal : strategic interests of actors • Dependency : one actor depends on another in order to attain some goal, execute some plan, or deliver a resource • Plan : represents a way satisfying a goal • Resource : represents a physical or an informational entity that one actor wants and another can deliver • Capability : represents the ability of an actor to define, choose and execute a plan fulfill a goal, given a particular operating environment • Belief : are used to represent each actor’s knowledge of the world The abstract syntax of the language is defined in terms of UML. AOSE

39. Implementing Issues EASSS AAMAS • Object-oriented tools: are very much related to the object-oriented approach • JADE (Java Agent DEvelopment framework) • BDI toolkits: are based on BDI models • ParADE (Parma Agent Development Environment) Toolkit for the development of FIPA agents • Agent level • Agents are atomic components • UML is used to build models of single agents and of the multiagent system • Object level, exploits the generated code: • Each agent is an object-oriented system • ParADE provides is a framework on top of JADE AOSE

40. Social Interaction With Embodied Agents EASSS AAMAS • Lewis Johnson Director, CARTE USC / ISI • The CARTE research group at USC has conducted extensive research in animated agents • Strengths and weakness of agents are attributable to SI (or lack thereof) • This has lead to a research focus on • Social interaction design • Social agent modeling

41. Embodied Interfaces EASSS AAMAS • Embodied, human-like interfaces are increasingly common • Animated characters • Humanoid robots • It is useful to model these as agents • To support dynamic interaction • Possible roles: • Guides, tutors, teammates, story characters • Applications: • Education • Commerce • Entertainment Lewis Johnson

42. Claims EASSS AAMAS • Embodied interfaces raise the expectations of • Ability to understand user’s problem solving • Social interaction skills, i.e., social intelligence • Animated agents can meet these expectations • Benefits: • Improved user performance • Improved subjective experience • In a mutually reinforcing way Lewis Johnson

43. Characteristics of Social Agents EASSS AAMAS • Cognizance of other agents • Sensitivity to social relationship, roles • Sensitivity to social context, exchange • Sensitivity to social norms • Holding beliefs, attitudes about others • Aware of beliefs, attitudes of others • Able to manage interactions, taking above into account Lewis Johnson

44. Social Intelligence Implies: EASSS AAMAS • Ability to model other agents • Ability to engage in natural interaction • Mixed-initiative interaction • Face-to-face interaction • “Emotional intelligence” • Ability express emotion appropriately • Ability to react to emotions in others • Ability to develop social relationships • Convergence and adaptation of behavior • Shared experience Lewis Johnson

45. Application of Animated Social Agents EASSS AAMAS • Virtual reality training environments • Application: military training • On-line education • Applications: medicine, dentistry, chemistry, engineering, elementary school science • Interactive pedagogical dramas • Applications: health education for adults and children, military training Lewis Johnson

46. Steve EASSS AAMAS Lewis Johnson

47. New version of Steve EASSS AAMAS Lewis Johnson

48. STEVE’s Social Intelligence EASSS AAMAS • Capabilities: • Use of verbal and nonverbal cues to indicate awareness • Face to face multimodal communication • Mixed initiative dialog • Appropriate emotional reactions • Capabilities as yet insufficient: • Ability to model the learner’s state, characteristics based on interactions • Ability to learn from interactions with the student Lewis Johnson

49. Steve Architecture EASSS AAMAS Cognition Lewis Johnson Perception snapshot, important events Abstract motor commands Motor Control Perception Spatial information Relevant events Detailed motor commands Simulated World Steve Body

50. Application: case-based health science courseware Monitors students, gives advice, hints, feedback Intervenes if the student makes serious mistakes Records and evaluates student performance Adele EASSS AAMAS Lewis Johnson