©Intelligent Agent Technology and Application, 2006, Ai Lab NJU Intelligent Agent Technology and Application Course overview and what is intelligent agent

Sept. 2006©Gao Yang, Ai Lab NJU 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

Sept. 2006©Gao Yang, Ai Lab NJU 3 General definitions American Heritage Dictionary – ”... One that acts or has the power or authority to act... or represent another” Russel and Norvig – ”An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.” Maes, Parrie – ”Autonomous agents are computational systems that inhabit some 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”.

Sept. 2006©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; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions.

Sept. 2006©Gao Yang, Ai Lab NJU 5 Agent: industrial definitions IBM – ”Intelligent agents are software entities that carry out some set of operations on 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’s goals or desires”

Sept. 2006©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.”

Sept. 2006©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

Sept. 2006©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)

Sept. 2006©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 – ……

Sept. 2006©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

Sept. 2006©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

Sept. 2006©Gao Yang, Ai Lab NJU 12 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.

Sept. 2006©Gao Yang, Ai Lab NJU 13 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.

Sept. 2006©Gao Yang, Ai Lab NJU 14 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.

Sept. 2006©Gao Yang, Ai Lab NJU 15 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.

Sept. 2006©Gao Yang, Ai Lab NJU 16 Discrete vs. continuous – An environment is discrete if there are a fixed, finite number of actions and percepts in it.

Sept. 2006©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.

Sept. 2006©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:

Sept. 2006©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).

Sept. 2006©Gao Yang, Ai Lab NJU 20 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

Sept. 2006©Gao Yang, Ai Lab NJU 21 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.

Sept. 2006©Gao Yang, Ai Lab NJU 22 Social ability – Must negotiate and cooperate with others.

Sept. 2006©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 methods on 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.

Sept. 2006©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.” Thinking it. – (implement agents using object-oriented technology)…… Thinking it.

Sept. 2006©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

Sept. 2006©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.

Sept. 2006©Gao Yang, Ai Lab NJU 27 Example of agents

Sept. 2006©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

Sept. 2006©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

Sept. 2006©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)

Sept. 2006©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

Sept. 2006©Gao Yang, Ai Lab NJU 32 Benefits of MAS Faster problem solving Decreasing communication – Higher semantics of communication (speech-act level) Flexibility Increasing reliability

Sept. 2006©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)

Sept. 2006©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 independent designer – Agents have their own agenda and motivation – Concerned with the benefit of each agent (’individualistic’) – The latter more realistic in an Internet-setting?

Sept. 2006©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.

Sept. 2006©Gao Yang, Ai Lab NJU 36 Distributed AI perspectives

Sept. 2006©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 …..

Sept. 2006©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

Sept. 2006©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.

Sept. 2006©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.

Sept. 2006©Gao Yang, Ai Lab NJU 41 Coursework 3. Please determine the environment’s type. ChessPokerMine- sweeper E- shopping Accessible?? Deterministic ?? Episodic?? Static?? Discrete??