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  1. Agent Technology for e-Commerce Chapter 2: Software Agents Maria Fasli http://cswww.essex.ac.uk/staff/mfasli/ATe-Commerce.htm

  2. What is an agent? • A piece software (and/or hardware) that acts on behalf of the user • Unfortunately there is no unique and universally accepted definition of what constitutes an agent • Different characteristics are important for different domains of application

  3. Defining agents • “An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors” (Russell and Norvig 2003) • “Agents are active, persistent (software) components that perceive, reason, act and communicate” (Huhns and Singh 1997) • “an entity that functions continuously in an environment in which other processes take place and other agents exist” (Shoham 1997) • “Autonomous agents are computational systems that inhabit some complex environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks that they are designed for” (Maes 1995)

  4. Characteristics of agents • Although there is no agreement regarding the definitive list of characteristics for agents, among the most important seem to be: • Autonomy • Proactiveness • Reactiveness • Social ability

  5. Autonomy • Difficult to pin down exactly: how self-ruled the agent really is • An autonomous agent is one that can interact with its environment without the direct intervention of other agents and has control over its own actions and internal states • The less predictable an agent is the more autonomous it appears to be to an external observer • Absolute autonomy (complete unpredictability) may not be desirable; travel agent may exceed the allocated budget • Restrictions on autonomy via social norms

  6. Proactiveness • Proactive (goal-directed) behaviour: an agent actively seeks to satisfy its goals and further its objectives • Simplest form is writing a procedure or method which involves: • Preconditions that need to be satisfied for the procedure/method to be executed • Postconditions which are the effects of the correct execution of the procedure • If the preconditions are met and the procedure executes correctly, then the postconditions will be true

  7. Reactiveness • Goal-directed behaviour as epitomised via the execution of procedures makes two limiting assumptions: • while the procedure executes the preconditions remain valid • the goal and the conditions for pursuing such a goal, remain valid at least until the procedure terminates • Not realistic in dynamic, complex and uncertain environments • Agents must not blindly attempt to achieve their goals, but should perceive their environment and any changes that affect their goals and respond accordingly • Building an agent that achieves a balance between proactive and reactive behaviour is difficult

  8. Social ability • Agents are rarely isolated entities, they usually live and act in an environment with other agents, human or software • Social ability means being able to operate in a multi-agent environment and coordinate, cooperate, negotiation and even compete with others • This social dimension of the notion of agency must address many difficult situations which are not yet fully understood, even in the context of human behaviour

  9. Agents as intentional systems • Trying to understand and analyze the behaviour of complex agents in a natural, intuitive and efficient way is a nontrivial task • Methods that abstract us away from the mechanistic and design details of a system may be more convenient • Intentional stance: ascribing to a system human mental attitudes (anthropomorphism), e.g. beliefs, desires, knowledge, wishes

  10. How useful/legitimate is this approach? McCarthy explains: To ascribe certain ‘beliefs’, ‘knowledge’, ‘free will’, ‘intentions’, consciousness’, ‘abilities’ or ‘wants’ to a machine or computer program is legitimate when such an ascription expresses the same information about the machine that it expresses about a person. It is useful when the ascription helps us understand the structure of the machine, its past or future behaviour, or how to repair or improve it.

  11. The intentional stance provides us with a powerful abstraction tool: the behaviour of systems whose structure is unknown can be explained • Computer systems or programs are treated as rational agents. • An agent possesses knowledge or beliefs about the world it inhabits, it has desires and intentions and it is capable of performing a set of actions • An agent uses practical reasoning and based on its information about the world and its chosen desires and intentions will select an action which will lead it to the achievement of one of its goals

  12. Action Environment Sensory information Making decisions • We require software agents to whom complex tasks and goals can be delegated • Agents should be smart so that they can make decisions and take actions to successfully complete tasks and goals • Endowing the agent with the capability to make good decisions is a nontrivial issue

  13. A simple view of an agent • Environment states S={s1, s2, …} • Perception see:S→P • An agent has an internal state (IS) which is updated by percepts: next:ISP→IS • An agent can choose an action from a set A={a1, a2, …}: action:IS →A • The effects of an agent’s actions are captured via the function do: do:A S →S

  14. The control loop of such an agent would look as follows:

  15. Characteristics of the environment • The nature of the environment has a direct impact on the design of an agent and its decision-making process • It can be characterized as: • Fully or partially observable • Deterministic or stochastic • Static or dynamic • Episodic or sequential • Discrete or continuous • Single-agent or multi-agent

  16. Fully vs partially observable environments • Observability describes access to information about the world • In a fully observable environment an agent has complete access to its state and can observe any changes as they occur in it • Most realistic environments are only partially observable • Partial observability can be attributed to noise in the agent’s sensors or perceptual aliasing • Partial observability is important in multi-agent systems as it also affects what the agent knows about the other agents • The more information an agent has about its world, the easier it is to choose and perform the best action

  17. Deterministic vs stochastic environments Deterministic environments • The next state is completely determined by the current state and the actions performed by the agent • The outcome of an agent’s actions is uniquely defined, no need to stop and reconsider Most environments are stochastic • There is a random element that decides how the world changes • Limited sphere of influence: the effects of an agent’s actions are not known in advance • An agent’s actions may even fail • Stochasticity complicates agent design

  18. Static vs dynamic environments Static environments • The world only changes by the performance of actions by the agent itself • If an agent perceives the world at time t0 and the agent performs no action until t1, the world will not change Dynamic environments • The world constantly changes • Even when an agent is executing an action a with a precondition p which holds true before the execution, p may not be true at some point during the execution • The outcome of an agent’s action cannot be guaranteed as other agents and the environment itself may interfere

  19. It is more difficult to build agents for dynamic environments • Issue: the agent needs to do information gathering often enough in order to have up-to-date information about the world; this depends on the rate of change of the environment • An agent also needs to take into account the other agents and synchronize and coordinate its actions with theirs in order to avoid interference and conflicts

  20. Episodic vs sequential environments In an episodic environment • A cycle of perception and then action is considered to be a separate episode • The agent’s performance depends only on the current episode • The agent need not worry about the effects of its actions on subsequent episodes and need not think ahead In sequential environments each decision made affects the next one • How an environment is characterized depends on the level of abstraction

  21. Discrete vs continuous environments In a discrete environment • There is a fixed, finite number of actions and percepts • In principle, one can enumerate all possible states and the best action to perform in each of these – not practical though In continuous environments • The number of states may be infinitely long

  22. Single-agent vs multi-agent environments • In a single-agent environment there is one agent operating whereas in multi-agent environments there are many agents that interact with each other • But, at times objects or entities that we would not normally consider as agents may have to be modelled as such • Nature may be modelled as an agent • Usually any entity/object that affects or influences the behaviour of the agent under consideration needs to be regarded as an agent

  23. Open environments • The most complex class of environments are those that are partially observable, stochastic, dynamic, sequential, continuous and multi-agent • Known as open environments

  24. Performance measure • Objective: develop agents that perform well in their environments • A performance measure indicates how successful an agent is • Two aspects: how and when

  25. How is performance assessed: • Different performance measures will be suitable for different types of agents and environments • Contrast a trading agent with a vacuum cleaning agent • Objective performance measures are defined by us as external observers of a system When is performance assessed: • Important • Continually, periodically or one-shot

  26. Goal states • One possible way to measure how well an agent is doing is to check that it has achieved its goal • There may be a number of different action sequences that will enable an agent to satisfy its goal • A good performance measure should allow the comparison of different world states or sequences of states

  27. Preferences and utilities • Agents need to be able to express preferences over different goal states • Each state s can be associated with a utility u(s) for each agent • The utility is a real number which indicates the desirability of the state for the agent • For two states s and s’ • agent i prefers s to s’ if and only if u(s)>u(s’) • is indifferent between the two states if and only if u(s)=u(s’) • The agent’s objective is to bring about states of the environment that maximise its utility

  28. Maximum Expected Utility • In a stochastic environment the performance of an action may bring about any one of a number of different outcomes • The expected utility of an action a is: • The agent then chooses to perform action a* which has the maximum expected utility (MEU):

  29. A complete specification of the utility function allows rational decisions when: • there are conflicting goals, only some of which can be accomplished; the utility function indicates the appropriate tradeoff; • there are several goals that the agent can endeavour to achieve, but none of which can be achieved with certainty; the utility provides a way in which the likelihood of success can be evaluated against the importance of the goals

  30. Rationality What is rational at any given time depends on: • the performance measure that determines the degree of success • everything that the agent has perceived so far • what the agent expects to perceive and happen in the future • what the agent knows about the environment • the actions that the agent can perform Definition: An ideal rational agent performs actions that are expected to maximize its performance measure

  31. Bounded rationality • Making a decision requires computational power, memory and computation takes time • Agents are resource-bounded and this has an impact on their decision-making process: optimal decision making may not be possible • Ideal rationality may be difficult to achieve • Bounded rationality • restrictions on the types of options may be imposed • the time/computation for option consideration may be limited • the search space may be pruned • the option selected will be strategically inferior to the optimal one

  32. Rational decision making and optimal policies • An agent is situated in an environment where there may be other agents present • Time can be measured in discrete time points T={1,2,…} • The environment is in a state st at time t and the set of world states is indicated by S • The agent perceives its environment through a percept pt • The agent affects its environment by performing an action at • A policy  is a complete mapping from states to actions • Given a state, a policy tells an agent what action to perform • An agent may take into account information about the past and the future

  33. Taking into account the past • Information about the past: percept and action pairs (pt,at) • An agent’s policy: ((p1,a1),(p2,a2),…,pt)=at • This can be problematic (i) history may be too large, and (ii) computing an optimal policy  from a computational complexity point of view would be nontrivial

  34. Markov environments • In some environments, the state of the world at time t provides a complete description of the history before t, hence pt=st • All necessary information to decide on an optimal action is in pt • Such a world state is said to be Markov or have the Markov property • An agent’s policy is: (pt)=at or (st)=at • Such an agent that can ignore the past is called a reactive agent and its policy reactive or memoryless

  35. Taking into account the future • In a discrete world the agent performs an action a at each time point t, and the world changes as a result of this in t+1 • A transition model T(s,a,s’) describes how the world s changes as a result of an action a being performed • In a deterministic environment • the transition model maps (s,a) to a single resulting state s’ • a number of approaches exist to plan ahead • In a stochastic environment: • the transition model is T(s,a,s)= P(s’|s,a) • Graph search is not applicable as there is uncertainty about the transitions between states

  36. MDPs and POMDPs • The problem of calculating an optimal policy in a fully observable, stochastic environment with a transition model that satisfies the Markov property, is called a Markov decision problem (MDP) • In a partially observable environment, pt provides limited information and the agent cannot determine in which state the world really is • The problem of calculating an optimal policy in a partially observable environment is called a partially observable Markov decision problem (POMDP) • Methods for solving MDPs are not directly applicable to POMDPs

  37. Optimal policies in MDPs • Given a MDP, one can calculate a policy from the transition model (probabilities) and the utility function • Assume a stochastic, single-agent world with transition model P(s’|s,a). The agent should choose an optimal action a*: • The optimal policy is • But to be able to calculate the policy we need to know the utilities of all states

  38. 1 2 3 3 +1 2 -1 1 Start Example • The agent can move North, South, East and West • Bumping onto a wall leaves the position unchanged • Fully observable and stochastic: every action to the intended direction succeeds with p=0.8, but with probability p=0.2 the agent moves at right angles towards the intended direction • Only the utilities of the terminal states are known

  39. The utility function needs to be based on a state sequence u([s0,…,sn]) instead of a single state • To use the MEU rule, the utility function needs to be separable u([s0,…,sn]) =R(s0,)+ u([s1,…,sn]) where R(s0,) is called the reward function

  40. The immediate reward for each of the non-terminal states -0.04 • The utility u(s) of a state is defined as • The rewards of the terminal states are propagated out through all the other states • Known as the Bellman equation forms the basis of dynamic programming • Calculating the utilities in dynamic programming is an n-step decision problem

  41. Two other methods of calculating optimal policies in MDPs are: • Value iteration: starting with a transition model and a reward function, calculate a utility for each state, and then use this to create a policy • Policy iteration: start with some policy, and then repeatedly calculate the utility function for that policy, and then use it to calculate a new policy, and so on. A policy usually converges long before the utility function does.

  42. 1 2 3 1 2 3 3 0.87 0.93 3 +1 +1 0.82 0.78 2 2 -1 -1 1 0.76 0.72 0.49 1 (a) (b) The utilities have been calculated using the value iteration method

  43. Planning • A key ability for intelligent behaviour as it increases an agent’s flexibility enabling it to construct sequences of actions to achieve its goals • Planning is deciding what to do; closely related to scheduling which is deciding when to do an action • The agent needs to figure out the list of steps (subtasks) that need to be carried out in order to achieve its goal • Different approaches to planning

  44. Approaches to planning • Logic-based approaches: such as Situation Calculus • Case-based approaches: previously generated plans are stored in a library and can be reused to solve similar problems in the future • Operator-based approaches: such as STRIPS

  45. Planning as search: • Situation space search: find a path from the initial state to the goal state • Progression: forward search • Regression: backward search from the goal state • Plan space search: search through the space of possible plans • Total order planning (totally-ordered sequence of actions) • Partial order planning or least-commitment planning (partially-ordered set of actions)

  46. Learning • Learning: the process of acquiring knowledge, skills, or attitudes through experience, imitation, or teaching, which then causes changes in the behaviour • Important in open, dynamic and constantly evolving environments that electronic marketplaces are

  47. Why is learning important • If we succeed in endowing agents with the ability to learn, this may also offer important insights into how humans learn • Impractical or even impossible to specify agent systems correctly and completely at the time of design and implementation • It may not be practical to build all intelligence into the system in advance • There may be hidden relationships and correlations among huge amounts of data which may not be easy to discern • Produced systems may not work as well as desired or expected in the environments in which they are used

  48. Knowledge about certain tasks may simply be too large to be explicitly encoded by humans • Agents that operate in dynamic environments have to be able to cope with the constant changes • Any initial knowledge and skills encoded at the development stage with time it may become useless or out-of-date as the environment changes • The nature of the task may change • The environment may change in such a way so that the agent’s goals need to be changed as well

  49. Learning methods • Learning takes place as a result of the interaction between an agent and the environment and from observing one’s own decision-making • Isolated (centralized) learning • Learning with others (decentralized or interactive learning) • Through knowledge acquisition (rote, memorization) • From instruction and through advice taking • From examples and practice • By analogy • Through problem-solving • By discovery – the most difficult type of learning to implement

  50. To improve the learning process feedback may be provided: • Supervised learning: the feedback specifies the desired activity and the objective is to match this desired action as closely as possible • Reinforcement learning: the feedback only specifies the utility of the learner’s activity and the goal is to maximise this utility • Unsupervised learning: no explicit feedback is provided and the objective is to learn useful and desired skills and activities through a process of trial and error