Intelligent Agent Technology and Application

1. Intelligent AgentTechnology and Application Course overview and what is intelligent agent

2. What is intelligent agent • Field that inspired the agent fields? • Artificial Intelligence • Agent intelligence and micro-agent • Software Engineering • Agent as an abstracted entity • Distributed System and Computer Network • Agent architecture, MAS, Coordination • Game Theory and Economics • Agent Negotiation • There are two kinds definition of agent • Often quite narrow • Extremely general ©Gao Yang, Ai Lab NJU

3. General definitions • American Heritage Dictionary • ”... One that acts or has the power or authority to act... or representanother” • Russel and Norvig • ”An agent is anything that can be viewed as perceiving its environment through sensors andacting upon that environment through effectors.” • Maes, Parrie • ”Autonomous agents are computational systems that inhabitsome complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed”. ©Gao Yang, Ai Lab NJU

4. Agent: more specific definitions • Smith, Cypher and Spohrer • ”Let us define an agent as a persistent software entity dedicated to a specific purpose. ’Persistent’ distinguishes agents from subroutines; agents have their own ideas about how to accomplish tasks, their own agendas. ’Special purpose’ distinguishes them from multifunction applications; agents are typically much smaller. • Hayes-Roth • ”Intelligent Agents continuously perform three functions: perception of dynamic conditions in the environment; actionto affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. ©Gao Yang, Ai Lab NJU

5. Agent: industrial definitions • IBM • ”Intelligent agents are software entities that carry out some set of operationson behalf of a user or another program with some degree of independence or autonomy, and in doing so, employ some knowledge or representations of the user’sgoals or desires” ©Gao Yang, Ai Lab NJU

6. Agent: weak notions • Wooldridge and Jennings • An Agent is a piece of hardware or (more commonly) software-based computer system that enjoys the following properties • Autonomy: agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state; • Pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative. • Reactivity: agents perceive their environment and respond to it in timely fashion to changes that occur in it. • Social Ability: agents interact with other agents (and possibly humans) via some kind of agent-communication language.” ©Gao Yang, Ai Lab NJU

7. Agent: strong notions • Wooldridge and Jennings • Weak notion in addition to • Mobility: the ability of an agent to move around a network • Veracity: agent will not knowingly communicate false information • Benevolence: agents do not have conflicting goals and always try to do what is asked of it. • Rationality: an agent will act in order to achieve its goals and will not act in such a way as to prevent its goals being achieved ©Gao Yang, Ai Lab NJU

8. Summary of agent definitions • An agent act on behalf user or another entity. • An agent has the weak agent characteristics. (Autonomy, Pro-activeness, Reactivity, Social ability) • An agent may have the strong agent characteristics. (Mobility, Veracity, Benevolence, Rationality) ©Gao Yang, Ai Lab NJU

9. Dear child gets many names… • Many synonyms of the term “Intelligent agent” • Robots • Software agent or softbots • Knowbots • Taskbots • Userbots • …… ©Gao Yang, Ai Lab NJU

10. Autonomy is the key feature of agent • Examples • Thermostat • Control / Regulator • Any control system • Software Daemon • Print server • Http server • Most software daemons ©Gao Yang, Ai Lab NJU

11. Type of environment • An agent will not have complete control over its environment, but have partial control, in that it can influence it. • Scientific computing or MIS in traditonal computing. • Classification of environment properties [Russell 1995, p49] • Accessible vs. inaccessible • Deterministic vs. non-deterministic • Episodic vs. non-episodic • Static vs. dynamic • Discrete vs. continuous ©Gao Yang, Ai Lab NJU

12. Accessible vs. inaccessible • Accessible vs. inaccessible • An accessible environment is one in which the agent can obtain complete, accurate, up-to-date information about the environment’s state. (also complete observable vs. partial observable) • Accessible: sensor give complete state of the environment. • In an accessible environment, agent needn’t keep track of the world through its internal state. ©Gao Yang, Ai Lab NJU

13. Deterministic vs. non-deterministic • Deterministic vs. non-deterministic • A deterministic environment is one in which any action has a single guaranteed effect , there is no uncertainty about the state that will result from performing an action. • That is, next state of the environment is completely determined by the current state and the action select by the agent. • Non-deterministic: a probabilistic model could be available. ©Gao Yang, Ai Lab NJU

14. Episodic vs. non-episodic • Episodic vs. non-episodic • In an episodic environment, the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios. It need not reason about the interaction between this and future episodes. (such as a game of chess) • In an episodic environment, agent doesn’t need to remember the past, and doesn’t have to think the next episodic ahead. ©Gao Yang, Ai Lab NJU

15. Static vs. dynamic • Static vs. dynamic • A static environment is one that can assumed to remain unchanged expect by the performance of actions by the agents. • A dynamic environment is one that has other processes operating on it which hence changes in ways beyond the agent’s control. ©Gao Yang, Ai Lab NJU

16. Discrete vs. continuous • Discrete vs. continuous • An environment is discrete if there are a fixed, finite number of actions and percepts in it. ©Gao Yang, Ai Lab NJU

17. Why classify environments • The type of environment largely determines the design of agent. • Classifying environment can help guide the agent’s design process (like system analysis in software engineering). • Most complex general class of environments • Are inaccessible, non-deterministic, non-episodic, dynamic, and continuous. ©Gao Yang, Ai Lab NJU

18. Discuss about environment: Gripper • Gripper is a standard example for probabilistic planning model • Robot has three possible actions: paint (P), dry (W) and pickup (U) • State has four binary features: block painted, gripper dry, holding block, gripper clean • Initial state: • Goal state: ©Gao Yang, Ai Lab NJU

19. Intelligent agent vs. agent • An intelligent agent is one that is capable of flexible autonomous action in order to meet its design objectives, where flexibility means three things: • Pro-activeness: the ability of exhibit goal-directed behavior by taking the initiative. • Reactivity: the ability of percept the environment, and respond in a timely fashion to changes that occur in it. • Social ability: the ability of interaction with other agents (include human). ©Gao Yang, Ai Lab NJU

20. Pro-activeness • Pro-activeness • In functional system, apply pre-condition and post-condition to realize goal directed behavior. • But for non-functional system (dynamic system), goal must remain valid at least until the action complete. • agent blindly executing a procedure without regard to whether the assumptions underpinning the procedure are valid is a poor strategy. • Observe incompletely • Environment is non-deterministic • Other agent can affect the environment ©Gao Yang, Ai Lab NJU

21. Reactivity • Reactivity • Agent must be responsive to events that occur in its environment. • Building a system that achieves an effective balance between goal-directed and reactive behavior is hard. ©Gao Yang, Ai Lab NJU

22. Social ability • Social ability • Must negotiate and cooperate with others. ©Gao Yang, Ai Lab NJU

23. Agent vs. object • Object • Are defined as computational entities that encapsulate some state, are able to perform actions, or methodson this state, and communicate by message passing. • Are computational entities. • Encapsulate some internal state. • Are able to perform actions, or methods, to change this state. • Communicate by message passing. ©Gao Yang, Ai Lab NJU

24. Agent and object • Differences between agent and object • An object can be thought of as exhibiting autonomy over its state: it has control over it. But an object does not exhibit control over it’s behavior. • Other objects invoke their public method. Agent can only request other agents to perform actions. • “Objects do it for free, agents do it for money.” • (implement agents using object-oriented technology)……Thinking it. ©Gao Yang, Ai Lab NJU

25. Agent and object • In standard object model has nothing whatsoever to say about how to build systems that integrate reactive, pro-active, social behavior. • Each has their own thread of control. In the standard object model, there is a single thread of control in the system. • (agent is similar with an active object.) • Summary, • Agent embody stronger notion of autonomy than object • Agent are capable of flexible behavior • Multi-agent system is inherently multi-threaded ©Gao Yang, Ai Lab NJU

26. Agent and expert system • Expert system • Is one that is capable of solving problems or giving advice in some knowledge-rich domain. • The most important distinction • Expert system is disembodied, rather than being situated. • It do not interact with any environment. Give feedback or advice to a third part. • Are not required to interact with other agents. ©Gao Yang, Ai Lab NJU

27. Example of agents ©Gao Yang, Ai Lab NJU

28. Distributed Artificial Intelligence (DAI) • DAI is a sub-field of AI • DAI is concerned with problem solving where agents solve (sub-) tasks (macro level) • Main area of DAI • Distributed problem solving (DPS) • Centralized Control and Distributed Data (Massively Parallel Processing) • Multi-agent system (MAS) • Distributed Control and Distributed Data (coordination crucial) Some histories ©Gao Yang, Ai Lab NJU

29. DAI is concerned with…… • Agent granularity (agent size) • Heterogeneity agent (agent type) • Methods of distributing control (among agents) • Communication possibilities • MAS • Coarse agent granularity • And high-level communication ©Gao Yang, Ai Lab NJU

30. DAI is not concerned with…… • Issues of coordination of concurrent processes at the problem solving and representational level. • Parallel computer architecture, parallel programming languages or distributed operation system. • No semaphores, monitors or threads etc. • Higher semantics of communication (speech-act level) ©Gao Yang, Ai Lab NJU

31. Motivation behind MAS • To solve problems too large for a centralized agent • E.g. Financial system • To allow interconnection and interoperation of multiple legacy system • E.g. Web crawling • To provide a solution to inherently distributed system • To provide a solution where expertise is distributed • To provide conceptual clarity and simplicity of design ©Gao Yang, Ai Lab NJU

32. Benefits of MAS • Faster problem solving • Decreasing communication • Higher semantics of communication (speech-act level) • Flexibility • Increasing reliability ©Gao Yang, Ai Lab NJU

33. Heterogeneity degrees in MAS • Low • Identical agents, different resources • Medium • Different agent expertise • High • Share only interaction protocol (e.g. FIPA or KQML) ©Gao Yang, Ai Lab NJU

34. Cooperative and self-interested MAS • Cooperative • Agents designed by interdependent designers • Agents act for increased good of the system (i.e. MAS) • Concerned with increasing the systems performance and not the individual agents • Self-interested • Agents designed by independentdesigner • Agents have their own agenda and motivation • Concerned with the benefit of each agent (’individualistic’) • The latter more realistic in an Internet-setting? ©Gao Yang, Ai Lab NJU

35. Our categories about MAS • Cooperation • Both has a common object • Competitive • Each have different objects which are contradictory. • Semi-competitive • Each have different objects which are conflictive, but the total system has one explicit (or implicit) object The first now is known as TEAMWORK. ©Gao Yang, Ai Lab NJU

36. Distributed AI perspectives ©Gao Yang, Ai Lab NJU

37. Our Thinking in MAS • Single benefit vs. collective benefit • No need central control • Social intelligence vs. single intelligence • Self-organize system • Self-form, self-evolve • Intelligence is emergence, not innative • ….. ©Gao Yang, Ai Lab NJU

38. Conclusions of lecture • Agent has general definition, weak definition and strong definition • Classification of the environment • Differences between agent and intelligent agent, agent and object, agent and expert system • Multi-agent system is macro issues of agent systems ©Gao Yang, Ai Lab NJU

39. Coursework • 1. Give other examples of agents (not necessarily intelligent) that you know of. For each, define as precisely as possible: • (a). the environment that the agent occupies, the states that this environment can be in, and the type of environment. • (b). The action repertoire available to the agent, and any pre-conditions associated with these actions; • (c). The goal, or design objectives of the agent – what it is intended to achieve. ©Gao Yang, Ai Lab NJU

40. Coursework • 2. If a traffic light (together with its control system) is considered as intelligent agent, which of agent’s properties should be employ? Illustrate your answer by examples. ©Gao Yang, Ai Lab NJU

41. Coursework • 3. Please determine the environment’s type. ©Gao Yang, Ai Lab NJU