Robotics Summary. Made by: Iskaj Janssen

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1 Robotics Summary Made by: Iskaj Janssen Multiagent system: System composed of multiple agents. Five global computing trends: 1. Ubiquity (computers and intelligence are everywhere) 2. Interconnection (networked and distributed systems) 3. Intelligence (more complex tasks are automated) 4. Delegation (give control to computer systems) 5. Human-orientation (move away from machine-oriented views of programming, also progression in programming) Agents: Computer systems that: 1. Are capable of autonomous action for deciding for themselves what they need to do in order to satisfy design objectives. 2. Are capable of interacting with other agents, cooperation, coordination & negotiation. 3. Are reactive in the sense that they perceive their environment and respond in a timely fashion to the changes that occur in it. 4. Are proactive in the sense that they are not only reactive, but also exhibit goal-directed behavior by taking the initiative. Properties of intelligent agents: 1. Are mobile, can move around in an electronic network. 2. Are veracious/truthful, will not knowingly communicate false information.

2 3. Are benevolent, agents do not have conflicting goals; agents will try to do what is asked of them. 4. Are rational, agents will act in order to achieve its goals as far as it knows how to do that. 5. Can learn, agents improve over time. Import agent aspects: 1. Agents can fail in a multitude of ways: Sensor failure, perception failure, interpretation failure, etc. Classification of environment properties: Accessible Can obtain complete, accurate and up-to-date information about the environment state. Deterministic An action has a single guaranteed effect. Static An environment that only changes by the actions of the agent. Discrete An environment is discrete if there are a fixed, finite number of actions and percepts in it. Inaccessible Things like the Internet and the physical world are not accessible in this sense, thus inaccessible. Non-deterministic An action does not have an guaranteed effect, like the roll of a dice. Dynamic An environment that keeps changing, even without the effects of the agents. The Internet and the physical world are good examples. Continuous An environment containing an infinite amount of actions and percepts Adjustable autonomy and cooperation: Sometimes it is smart to transfer decision making to another agent. For example when: 1. Other agents are expected to yield higher benefits. 2. Uncertainty about the environment is high. 3. The decision might cause harm. 4. The agent is just not capable enough to decide on its own. Folk psychology: Predicting and explaining human behavior through the attribution of attitudes such as believing or wanting. These attitudes are called intentional notions.

3 Agents as intentional systems: The intentional stance represents a level of abstraction in which behavior can be described in terms of mental properties. There are Grades of these intentional systems: 1. First-order: The system has beliefs and desires, but no beliefs and desires about these beliefs and desires. 2. Second-order: The system is more sophisticated, it has beliefs and desires about beliefs and desires both those of others and its own. Abstract architectures for intelligent agents: The environment is a finite set E of discrete instantaneous states: E = {e, e`,..} Agents are assumed to have an array of possible actions, 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: r: e0 (α0)-e1 (α1)-e2 (α3)-e3 (α4)-e4 A state transformer function represents behavior that changes the environment: τ: R Ac q(e) If τ(r) = Ø then there are no possible successor states to r. in this case, we say the system has ended the run. Formally, we say an environment is a triple where E is a set of environment states, e0 is the initial state, and τ is a state transformer function. Env = <E,e0, τ > Formally, we say an agent is a function which maps runs to actions: Ag: R E Ac Formally, we say that a system is a pair containing an agent and an environment, any system will have a set of possible runs, we denote the set of runs of agent Ag in environment (we assume it only contains terminated runs) Env by: R(Ag, Env )

4 Purely reactive agents : Would look a little bit like this: action: E Ac The see function is the agent s ability to observe its environment. Output of the see function is a percept. see: E Per Which maps environment states to percepts, and action is now a function action: Per* A Which maps sequences of percepts to actions. Let I be the set of all internal states of the agent. The perception functions for a state-based agent is unchanged. see: E Per The action-selection function action is now defined as a mapping: action: I Ac From internal states to actions. An additional function next is introduced. It maps an internal state and percept to an internal state. next: I x Per I The agent control loop: 1. Agent starts in some initial internal state 0 i 2. Observes its environment state e, and generates a percept see(e) 3. Internal state of the agent is then updated via next function, becoming next ( i t, see ( e t )) 4. The action selected by the agent is action(next( i t, see ( e t ))) 5. Go to 2

How to let agents decide what to do? Associate utilities with individual states task of the agent is to bring about the states that maximize utility.

5 How to let agents decide what to do? Associate utilities with individual states task of the agent is to bring about the states that maximize utility. A task specification is a function: u : E R Or assign utility not to individual states but to runs themselves: u : R R R is in both cases a real number which is associated with either an environment state or a run. The optimal agent is in this case, the agent which maximizes utility. To assign utilities to run, we have to know if it failed or was successful. A value of 1 is good in this case. Predicate task specifications are denoted by Ψ. Ψ: R {0, 1} A task environment is a pair: < Env, Ψ> An agent succeeds in an environment if: R Ψ ( Ag, Env ) = R( Ag, Env ) Symbolic AI: The classical approach to building agents, view them as knowledge-based systems and bring all the associated methodologies of such systems to bear. Deliberative/agent architecture: Contains a symbolic model of the world and makes decisions via symbolic reasoning. Two problems to this approach:

6 1. The transduction problem: Translating the world into an accurate adequate symbolic description in time for that description to be useful. (Vision, speech understanding and learning) 2. The representation/reasoning problem: Symbolically represent information about complex real-world entities and processes; get agents to reason with this information in time. (Knowledge representation, automated reasoning, automatic planning) The frame problem: How to determine what changes and what doesn t change when an action is performed. Deductive reasoning agents: A way for an agent to decide what to do using theorem proving. Use logic to encode a theory stating the best action to perform in any given situation. With this kind of loop: 1. Try to find an action explicitly prescribed. 2. Try to find an action not excluded. 3. No action found. Problems: Decision making assumes static environment (dust sucker robot). Even if we use propositional logic it will be a WAY too expensive problem to solve. Typical solutions: Weaken the logic, use symbolic/non-logical representations, shift emphasis of reasoning from run time to design time. Planning: Involves issues of both Search and Knowledge Representation. Take the block world for example: - Actions: UNSTACK(a,b), STACK(a,b), PICKUP(a) - Predicates to describe the world: ON(a,b), ONTABLE(a,b) - Logical formulas to describe truths: [Ǝ x HOLDING(x) ] ARMEMPTY] Green s method: Add state variables to the predicates and use a function DO that maps actions and states into new states. DO: A x S S Example: DO(UNSTACK(x, y), S) is a new state Problems: World model has to contain everything the robot needs to know ( Closed world assumption). Representing a real-world situation and keeping it updated and consistent in a way that is computationally tractable.

7 Agent oriented programming (Shoham): Programming agents in terms of intentional notions like belief, commitment and intention. Will have 3 components: 1. A logic for specifying agents and describing their mental states. 2. An interpreted programming language for programming agents. 3. An agentification process for converting neutral applications into agents. Practical reasoning: Reasoning directed towards actions the process of figuring out what to do. Theoretical reasoning: Reasoning directed towards beliefs. Human practical reasoning: 1. Deliberation : Deciding what state of affairs we want to achieve, results in intentions. 2. Means-end reasoning : Deciding how to achieve these states of affairs. For agents: 1. Intentions pose problems for agents, who need to determine ways of achieving them. 2. Intentions provide a filter for adopting other intentions, which must not conflict. 3. Agents track the success of their intentions, and are inclined to try again if their attempts fail. 4. Agents believe their intentions are possible. 5. Under certain circumstances agents believe they will bring about their intentions. 6. Agents don t need to intend all the expected side effects of their intentions. Means-end reasoning: Agent needs: representation of goal, representation of its available actions and representation of the environment. With this it will generate a plan to achieve the goal, this is also called automatic programming. A plan is defined as a list of actions with variables replaced by constants. STRIPS: Use a goal stack to control your search. The system has a database and a goal stack, and it focuses attention solving the top goal. Basic strips idea: Place goal on the stack, place sub goals on that to in the end solve the first goal. Implementing practical reasoning:

8 1. Deliberation is optimal if it selects some intention to achieve. The problem that the selected intention is no longer optimal by the time the agent has fixed upon it is called calculative rationality. This is done by understanding the available options (OPTION GENERATION), and choosing (FILTERING) between them and committing to some. What is chosen are then the intentions. So, this agent will have overall optimal behavior in the following circumstances: a. When deliberation and means-ends reasoning take a vanishingly small amount of time. b. When the world is guaranteed to remain static while the agent is deliberating and performing means-ends reasoning. c. When an intention that is optimal when achieved at time t0 is guaranteed to remain optimal until time t2. Commitment strategies: 1. Blind commitment : Continue to maintain an intention until it believes the intention has been achieved. 2. Single-minded commitment : Continue to maintain an intention until it believes that either the intention has been achieved or it is no longer possible to achieve. 3. Open-minded commitment : Maintain an intention as long as it is still believed optimal. Optimal intention reconsideration: Always reconsidering intentions leads to the risk that you will never achieve your intentions. Not changing your intentions will lead to an unsolvable intention. There are two different reconsideration strategies (Kinny and Georgeff): 1. Bold agents : Never pause to reconsider intentions. 2. Cautious agents : Stop to reconsider after every action. Dynamism is in the environment is represented by the rate of world change: ɣ, low ɣ = good for bold agents, high ɣ, is good for cautious agents.

9 BDI (belief-desire-intention) agent: the PRS Procedural Reasoning System : Swarm intelligence: Two views on cognition: 1. World Perception Cognition Action 2. World Perception Action (Cognition is in the eye of the beholder)

Embodied Embedded Cognition: Bodily interaction with the environment is primary to cognition. The cognition system is contributing to the ongoing interaction with the environment.

10 Embodied Embedded Cognition: Bodily interaction with the environment is primary to cognition. The cognition system is contributing to the ongoing interaction with the environment. Intelligent behaving system important points: Seems quite intelligent, is quite stupid. Getting by with less is the key. Ecological niche: Goals, world and sensorimotor possibilities. Reflexes: Stimulus Event Event is proportional to the duration and intensity of the stimulus. Taxes: Move in relation to a stimulus at a particular orientation. Positive move to positive side of the stimuli. Negative move away from positive side of the stimuli. Fixed action patterns: A pattern of actions with a rigid order triggered by a specific stimulus. Once started it will be finished, regardless of environmental feedback. Sequencing of innate behaviors: Behavior coordination mechanisms through (self-created) environmental stimuli.

These four behavioral reflexes can lead to quite adaptive behavior. However, to pick up where they left you need: Learning: Acquisition or change in behavior due to experience.

11 These four behavioral reflexes can lead to quite adaptive behavior. However, to pick up where they left you need: Learning: Acquisition or change in behavior due to experience. It helps fine-tuning to environment. Four ways to acquire behavior: - Innate: To be born with behavior. - Sequence of innate behaviors (mating cycle in wasps). - Innate with memory: To be born with behavior that needs initialization. - Learn: Behaviors that don t necessarily have to be innate. Behavioral control mechanism: Innate releasing mechanisms (IRM), releaser is a control variable: if not set, no response to sensory inputs. Concurrent behavior: Affordances: Perceivable potentialities for action. (Neisser: We can directly perceive what we are capable of doing with objects.) Intelligence (according to Rodney Brooks): 1. Intelligent behavior can be generated without explicit representations of the kind that symbolic AI proposes. 2. Intelligent behavior can be generated without explicit reasoning of the kind that symbolic AI proposes. 3. Intelligence is an emergent property of certain complex systems.

12 4. Reactive/proactive tradeoff: We want our agent to be able to react to the new situation, in time for the reaction to be of some use. However, we do not want our agent to be continually reacting, and hence never focusing on a goal long enough to actually achieve it. 5. Real intelligence is situated in the world, not in disembodied systems such as theorem provers. 6. Intelligence arises of an agent s interaction with the environment, it is in the eye of the beholder. Intelligent robots (according to Rodney Brooks): - Multiple goals - Multiple sensors - Robustness - Extensibility Subsumption architecture: Hierarchy of task-accomplishing behaviors. Behaviors maps perceptual input directly to actions. Each behavior is a rather simple rule-like structure. Behavior competes with others to exercise control over the agent. Lower layers have more primitive kinds of behavior, and have precedence over layers further up the hierarchy. Computationally very simple. The lower the layer, higher the priority. Multiple behavior rules can fire at the same time. Suppression: Occurs at the input of a data channel, normal input is withheld. Inhibition: Cancels any output of a data channel. Lower level layers can inhibit or suppress higher layers. Higher level layers can subsume (take over) the roles of lower levels by modifying the output of lower level modules through suppression. Gradient fields

13 Reactive agent advantages: - Simplicity - Economy - Computational tractability - Robustness against failure - Elegance - Proper design methodology (modular, iterative) Reactive agent disadvantages: - Agents without environment models must have sufficient information available from local environment. - If decisions are based on local environment, how does it take into account non-local information. - Difficult to make them learn. - Since behavior emerges from component interaction plus environment it is hard to see how to engineer specific agents. - It is hard to engineer agents with large numbers of behaviors. Hybrid architectures: Deliberative and reactive agent combined

LECTURE 5: REACTIVE AND HYBRID ARCHITECTURES

Reactive Architectures LECTURE 5: REACTIVE AND HYBRID ARCHITECTURES An Introduction to MultiAgent Systems http://www.csc.liv.ac.uk/~mjw/pubs/imas There are many unsolved (some would say insoluble) problems